Technical Support Document for Technical Products 

Prepared by the Western Regional Air Partnership

(WRAP) in Support of Western Regional Haze Plans 

February 28, 2011

Prepared and Reviewed by:

Michael Feldman, EPA Region 6

Erik Snyder, EPA Region 6

Kevin Golden, EPA Region 8

Gail Tonnesen, EPA Region 8

Aaron Worstell, EPA Region 8

Larry Biland, EPA Region 9

Carol Bohnenkamp, EPA Region 9

Scott Bohning, EPA Region 9

Gregory Nudd, EPA Region 9

Robert Kotchenruther, EPA Region 10

Keith Rose, EPA Region 10

Tom Moore, Western Governors’ Association/WRAP
2

WRAP Regional Haze Technical Support Document (TSD)

Table of Contents

1. BACKGROUND AND INTRODUCTION TO WRAP TECHNICAL WORK ................... 3

2. DEVELOPMENT OF VISIBILITY BASELINES AND PLANNING GOALS................... 5

3. EMISSIONS INVENTORIES.............................................................................................. 12

4. METEOROLOGICAL MODELING ................................................................................... 24

5. VISIBILITY MODELING ................................................................................................... 29

6. SOURCE APPORTIONMENT MODELING...................................................................... 43

7. BART MODELING.............................................................................................................. 52

8. CONCLUSIONS - WRAP TECHNICAL WORK IN SUPPORT OF REGIONAL HAZE 

PLANNING .................................................................................................................................. 57

A. APPENDIX: ACCESSING WRAP TECHNICAL PRODUCTS ........................................ 59
3

1. Background and Introduction to WRAP Technical Work

A. Background 

Regional haze is visibility impairment at federal Class I areas, 118 of which are located in the 

Western U.S., about 75% of the national total of Class I areas. Regional haze is caused

overwhelmingly by fine particulates (PM2.5), with limited impacts from the PMcoarse fraction of 

PM10 at more dusty Class I areas. The greatest fraction of visibility impairment occurs when 

PM2.5 in the atmosphere scatters and absorbs light, thereby creating haze. PM2.5 can be emitted 

into the atmosphere directly as primary particulates, or it can be produced in the atmosphere 

from photochemical reactions of gas-phase precursors and subsequent condensation to form 

secondary particulates. Examples of primary PM2.5 include crustal materials and elemental 

carbon; examples of secondary PM2.5 include ammonium nitrate, ammonium sulfates, and 

secondary organic aerosols (SOA). Diameters of secondary PM2.5 aerosols are generally smaller 

than primary PM2.5, and because visible light is most affected by particles between 0.5 and 1 

micrometers, PM2.5 is very effective at scattering light and impairing visual air quality and the 

ability to perceive scenic vistas over the long distances found in Class I areas of the West. Light 

scattering is also greater for PM2.5 than for PMcoarse particles. Secondary PM2.5 plays an 

especially important role in visibility impairment. Moreover, the smaller secondary PM2.5 can 

remain suspended in the atmosphere for longer periods and is transported long distances, thereby 

contributing to regional-scale impacts of pollutant emissions on visibility.

The sources of PM2.5 are difficult to quantify because of the complex nature of their formation, 

transport, and removal from the atmosphere, as well as the interaction between primary and 

secondary emissions with aerosols, solar radiation, water vapor, and other meteorological 

variables in the atmosphere. This makes it difficult to simply use emissions data to determine 

which pollutants should be controlled to most effectively improve visibility. Photochemical air 

quality models offer opportunity to better understand the sources of PM2.5 by simulating the 

emissions of pollutants and the formation, transport, and deposition of PM2.5. If an air quality 

model performs well for an historical episode, the model may then be useful for identifying the 

sources of PM2.5 and helping to select the most effective emissions reduction strategies for 

attaining visibility goals. Although several types of air quality modeling systems are available, 

the gridded, three-dimensional, Eulerian models provide the most complete spatial representation 

and the most comprehensive representation of processes affecting PM2.5, especially for situations 

in which multiple pollutant sources interact to form PM2.5.

In Section 169A of the 1977 Amendments to CAA1

 1

, Congress set forth a program for protecting 

visibility in the nation’s national parks and wilderness areas. This section of the CAA 

establishes as a national goal the “...prevention of any future, and the remedying of any existing, 

impairment of visibility in mandatory Federal Class I areas which impairment results from 

manmade air pollution.” EPA promulgated a rule to address regional haze on July 1, 1999 (64 

FR 35713), the Regional Haze Rule (RHR). The RHR established the goal of achieving 

“natural” visibility conditions in all 156 federal Class I areas by 2064.

http://www.epa.gov/air/caa/
4

Because the pollutants that lead to regional haze can originate from sources located across broad 

geographic areas, EPA has encouraged the States and Tribes across the United States to address 

visibility impairment from a regional perspective. With the WRAP, formed in 1997 to 

implement the Grand Canyon Visibility Transport Commission’s recommendations to EPA2, 

four additional regional planning organizations (RPOs)3 were developed to address regional haze 

and related issues. One of the main objectives of the RPOs is to analyze available data and 

conduct pollutant transport modeling to assist the States in developing their regional haze plans. 

The WRAP RPO is a collaborative effort of State governments, tribal governments, and various 

federal agencies established to conduct data analyses, conduct pollutant transport modeling, and 

coordinate planning activities among the western States, addressing regional haze in the 

numerous and highly diverse Class I areas across the West, ranging from high and low, hot and 

cold - deserts, mountains, seashores, forests and plains. WRAP member State and Tribal 

governments include: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, New 

Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming. Tribal board 

members include Campo Band of Kumeyaay Indians, Confederated Salish and Kootenai Tribes, 

Cortina Indian Rancheria, Hopi Tribe, Hualapai Nation of the Grand Canyon, Native Village of 

Shungnak, Nez Perce Tribe, Northern Cheyenne Tribe, Pueblo of Acoma, Pueblo of San Felipe, 

and Shoshone-Bannock Tribes of Fort Hall. WRAP activities were completely open and 

inclusive as stipulated in the WRAP Charter4. Several additional tribes from across the WRAP 

region, the State of Nevada, numerous Western local air agencies, environmental groups, 

industry, academia, consulting firms, and the general public were closely involved, and provided 

major technical input contributions to the WRAP analysis process.

B. Technical Requirements for Regional Haze SIPs 

The RHR does not mandate specific milestones or rates of progress, but instead calls for States to 

establish goals for 10-year milestone increments that provide for “reasonable progress” toward 

achieving natural visibility conditions over a nominal 60-year period, to steadily and smoothly 

reduce emission to achieve natural visibility conditions. In setting Reasonable Progress Goals 

(RPGs) for each 10-year target, States must provide for an improvement in visibility for the most 

impaired days over the 10-year period of the SIP, and ensure no degradation in visibility for the 

least impaired days over the same period. In setting the RPGs for each 10-year period covered by 

a SIP, States must also compare the RPGs to the uniform rate of progress needed to reach natural 

visibility conditions by 2064, referred to as the “glide path”, which is the linear rate of reduction 

in visibility impairment (in deciviews) needed to achieve natural conditions by 2064. The end 

point of the glide path, EPA default natural conditions values, must be reviewed and selected or 

alternative values adopted in each 10-year SIP cycle, thus the slope of the glide path, the natural 

conditions target, and the necessary rate of emissions reduction progress can and will likely 

change over the nominal 60-year period specified in the RHR, as natural conditions are better 

understood and defined. 

 2 http://www.wrapair.org/WRAP/reports/GCVTCFinal.PDF

3 http://www.epa.gov/air/visibility/regional.html#thefive#thefive 4 http://www.wrapair.org/WRAP/charter/charter.html
5

According to the RHR, these first foundational Regional Haze SIPs must specifically identify 

and address the following elements to specify emissions controls and reasonable progress 

expected to be achieved between the 2000-04 baseline and 2018, for each federal Class I area:

i. Baseline Visibility Conditions for the period 2000-04

ii. Initial Natural Visibility Conditions estimates for 2064

iii. Uniform Rate of Progress between these baseline and natural conditions

iv. Best Available Retrofit Technology (BART) to be implemented before 2018

v. Current (2002 or 2000-04) and Future (2018) Emission Inventories

vi. Sources’ and Regions’ Contributions to Haze at each Class I area

vii. Reasonable Progress Goals to be achieved by 2018

The purpose of this TSD is to review the technical products developed by the WRAP for the 

States in the western region, in support of their RH SIPs. This TSD evaluated the methods and 

procedures used by WRAP to develop products that assisted the western regional States in 

addressing the required elements of a RH SIP. Specifically, this TSD reviewed the meteorology, 

visibility impairment, source apportionment, and BART modeling conducted by WRAP, and 

determined if these models met applicable guidelines or protocols, and met modeling standards 

at the time they were conducted.

C. Format and Structure of This Document

Many portions of this text come directly from existing WRAP documentation of their technical 

work. To delineate explanations of WRAP’s work from EPA review of that work, this document 

is structured such that EPA comments appear in bold face as either ‘Review Comments’ or 

‘Explanatory Comments’.

2. Development of Visibility Baselines and Planning Goals

A. Introduction

Under the Regional Haze Rule5, each State is required to demonstrate reasonable progress in 

visibility conditions for each of its Class I areas. The State is to determine a uniform rate of 

progress ("glide path", "glide slope") toward the goal of natural visibility conditions in 2064. 

Considering various statutory factors, the State is also to define a reasonable rate of progress, and 

compare this to the benchmark uniform rate; if projected progress is less than the uniform rate, 

then the State is to explain why. Procedures for assessing progress are described in the Regional 

Haze Rule and EPA guidance documents.

In brief, the guidance defines a metric to quantify visibility conditions, together with procedures 

for determining a starting point and an ending point, between which progress is to be made. The 

metric used is the Haze Index, measured in deciviews, and is designed to correspond to human 

perception of visibility changes. It is defined as 10*ln(bext/10), where bext is extinction, the 

fraction of light scattered out of a sight path due to pollutants over a given distance (with units of 

 5 http://www.epa.gov/fedrgstr/EPA-AIR/1999/July/Day-01/a13941.pdf
6

Mm-1 or "inverse megameters"); it is inversely related to visual range. A 24-hour average is 

used, so there is a deciview value for each day of the year; the average of the 20% most-impaired 

days and of the 20% least-impaired days are to be assessed. The CAA national visibility goal for 

federal Class I areas is defined in the Regional Haze Rule, which is to improve visibility on the 

average of the worst 20% of days, while having no degradation on the average of the best 20%. 

The national goal is to be achieved by 2064 with steady incremental progress in the interim.

The starting point for progress is current or baseline visibility conditions, as monitored by the 

Interagency Monitoring of PROtected Visual Environments (IMPROVE) monitoring network. 

Monitored pollutant concentrations are converted to visibility extinction using the original or the 

revised IMPROVE equation, which adds up the contribution of each pollutant to extinction, 

while accounting for the effect of relative humidity; this is then converted to deciviews in the 

Haze Index. For each of the years 2000-04 of the baseline period, the average of the deciviews 

on the worst 20% of days is calculated; the five-year average of these defines the baseline. This 

procedure is described in detail in EPA's "Guidance for Tracking Progress Under the Regional 

Haze Rule" (hereafter “GTR”6). The guidance also makes provision for dealing with missing 

data, since monitoring instrument maintenance and malfunctions mean that data is not available 

for every scheduled measurement.

The end point for progress is the goal of natural visibility conditions in 2064. The default 

approach for determining these is described in EPA's "Guidance for Estimating Natural Visibility 

Conditions Under the Regional Haze Program" (hereafter “GENVC”7). Starting with annual 

average natural background pollutant concentrations as estimated by Trijonis et al. (1990)8 under 

NAPAP for the East and West parts of the country, deciviews are calculated with the IMPROVE 

equation, using the monthly relative humidity for each specific Class I area. These annual 

averages were then translated into estimates for the best 20% and worst 20% days needed for the 

progress assessment. Extinction was assumed to have a lognormal frequency distribution; 

deciviews would then have a normal distribution, and its 10th and 90th percentiles were used as 

estimates of the average of the best 20% and worst 20% of days, respectively. The result is a 

table of best and worst 20% deciview values for each Class I area, which appears in Appendix B 

of the guidance. The guidance also allows States to use a refined alternative to this default 

approach for estimating natural conditions.

Finally, the uniform rate of progress is the difference between the baseline and natural 

conditions, spread over the 60 years between 2004 and 2064: uniform deciviews per year 

improvement = (current 2004 deciviews - natural 2064 deciviews) / 60. This rate is the 

benchmark against which visibility improvement is to be compared by the State; the first 

 6 EPA, 2003, Guidance for Tracking Progress Under the Regional Haze Rule, EPA-454/B-03-004, September 2003, 

EPA OAQPS; web page: http://www.epa.gov/ttn/oarpg/t1pgm.html

direct link: http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_tpurhr_gd.pdf 7 EPA, 2003, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Program, EPA454/B-03-005, September 2003, EPA OAQPS; web page: http://www.epa.gov/ttn/oarpg/t1pgm.html 

direct link: http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_envcurhr_gd.pdf 8 Trijonis, J.C., et al., 1990, "Visibility: Existing and Historical Conditions-Causes and Effects", chapter 24 in 

NAPAP State of Science & Technology, Vol. III; web page: 

http://vista.cira.colostate.edu/improve/Publications/Principle_pubs.htm
7

planning period envisaged by the Regional Haze Rule is through 2018, so this uniform rate is 

multiplied by 14 to determine the first benchmark.

B. WRAP Procedures

The WRAP procedure for developing a uniform rate of progress (URP, also known as "glide 

path" or "glide slope") differed from the EPA default procedure in three principal ways: A. 

revised IMPROVE equation, B. alternative data substitution methods, and C. refined estimate of 

natural conditions

The procedures used by the WRAP for determining the uniform rate of progress or "glide path" 

are consistent with those in the Regional Haze Rule and EPA guidance. They varied in some 

particulars, in order to address some issues raised about the EPA default approaches. These 

issues were thought to be especially important for the Class I areas in the western states 

comprising the WRAP, for which visibility is generally already fairly good, natural visibility 

conditions are quite clean, and the effects of wildland fire is not well represented in the EPA 

default natural conditions estimates. 

C. Revised IMPROVE Equation

WRAP used the revised IMPROVE equation for estimates of both baseline and natural 

conditions. The IMPROVE equation is used to convert measured concentrations into extinction 

for each pollutant chemical species, and then total them up, accounting for the effect of relative 

humidity, and including the Rayleigh scattering that occurs in pure air. The extinction total is 

then used to calculate deciviews for use in visibility progress assessments. In December 2005 

the IMPROVE Steering Committee revised the IMPROVE equation after a scientific assessment 

of its implications for regional haze planning. In particular, when compared to nephelometer 

direct measurements of visibility extinction, the original IMPROVE equation over-predicts for 

low extinction conditions and under-predicts for high extinction. These biases have direct 

relevance for estimates for the best 20% and worst 20% visibility days that are used to assess 

progress. 

Old IMPROVE equation:

bext = 3 * f(RH) * [sulfate]

+ 3 * f(RH) * [nitrate]

+ 4 * [organic mass]

+ 10 * [elemental carbon]

+ 1 * [fine soil]

+ 0.6 * [coarse mass]

+ 10

Each term in the equation is the extinction due to a particular measured component; bracketed 

quantities are concentrations of as measured at IMPROVE monitors. The organic mass is 

assumed to be 1.4 times the organic carbon mass that is measured by IMPROVE monitors. The 

10 is for Rayleigh scattering which is due to the interaction of light with molecules of air itself 
8

with no pollutants, and is assumed to be the same for all locations, The f(RH) is a water growth 

factor for sulfate and nitrate, which are hygroscopic (their particles tend to attract water). Its 

value depends on relative humidity, ranging from 1 at low humidity to 18 at 98% humidity.

New IMPROVE equation:

bext = 2.2 * fs(RH) * [small sulfate] + 4.8 * fL(RH) * [large sulfate]

+ 2.4 * fs(RH) * [small nitrate] + 5.1 * fL(RH) * [large nitrate]

+ 2.8 * [small organic mass] + 6.1 * [large organic mass]

+ 10 * [elemental carbon]

+ 1 * [fine soil]

+ 1.7 * fss(RH) * [sea salt]

+ 0.6 * [coarse mass]

+ Rayleigh scattering (site-specific)

+ 0.33 * [NO2 (ppb)]

Sulfate is assumed to be all “large sulfate” if total sulfate is over 20 μg/m3, otherwise its fraction 

of the total is assumed to increase uniformly between 0 and 1 when the total is in the range 

between 0 and 20. I.e. large sulfate = (total sulfate/20)*total. A similar definition applies for 

nitrate and for organic mass. The organic mass is assumed to be 1.8 times the organic carbon 

mass that is measured by IMPROVE monitors, an increase over the old 1.4. Sea salt is estimated 

as 1.8 * [chloride] (or chlorine if chloride not available). Finally, the fs, fL, fss are water growth 

factors for small (“S”) and large (“L”) fractions of sulfate and nitrate, and for sea salt (“SS”). 

Their values depend on relative humidity, ranging from 1 at low humidity to over 5 at 95% 

humidity.

The new equation has five changes: 1) greater completeness though the inclusion of sea salt, 

which can be important for coastal sites; 2) increased organic carbon mass estimate, based on 

more recent data for remote areas; 3) Rayleigh scattering using site-specific elevation and 

temperature, a refinement over the older network-wide constant; 4) separate estimates for small 

and large particles of visibility impacts and humidity-dependent particle size growth rates, which 

could affect estimates at the low and high ends; and 5) greater completeness though the inclusion 

of NO2 (Pitchford, 2006)9.

The new equation shows broader scatter overall, but less bias in matching visibility 

measurements under high and low visibility conditions. That is, though it has a somewhat worse 

fit considering all the data, it has a better fit under visibility conditions most relevant to regional 

haze planning, the best and worst 20% of days. The looser overall fit can cause a slightly 

different set of days to be the ones chosen as the 20% worst, but the chemical species 

composition for such days is little changed (IMPROVE technical subcommittee for algorithm 

review, 2001, pp. 11-12), and so this makes little difference for assessing the contribution of 

emission sources to current conditions, and for projecting the effect of emission controls. The 

 9 Pitchford, Marc, 2006, "New IMPROVE algorithm for estimating light extinction approved for use", The 

IMPROVE Newsletter, Volume 14, Number 4, Air Resource Specialists, Inc.; web page: 

http://vista.cira.colostate.edu/improve/Publications/news_letters.htm

direct link: http://vista.cira.colostate.edu/improve/Publications/NewsLetters/IMPNews4thQtr2005.pdf
9

split between small and large particles was the main factor in reducing the biases. Another effect 

of choosing a revised equation with broader scatter of nephelometer measurements (typically 

resolved to ~5-minute average data) versus IMPROVE particle sampling (24-hour averages 

every 3rd-day) is that a typical viewer at a Class I area would be more likely to notice a 

difference between the visibility condition from the observed nephelometer value and the 

calculated IMPROVE value, although not critical to the regional haze planning process specified 

in the RHR.

The organic carbon (OC) measured by the IMPROVE network does not include all organic 

matter (OM); based on 1970s urban data, a scaling factor of 1.4 is embedded in the old equation 

to account for the full mass. Based on recent data more relevant to relatively remote Class I 

areas, the revised IMPROVE equation embeds an OM/OC factor of 1.8. In practice, for the 

worst days the biggest effect of switching to the revised IMPROVE equation is this increased 

organic carbon mass, since the worst days are dominated by organic carbon from fires, rather 

than the sulfates and nitrates that come more from anthropogenic sources.

Review Comment: The revised IMPROVE equation has less bias, is more refined, accounts for 

more pollutants, incorporates more recent data, and is based on considerations of relevance for 

the calculations needed for assessing progress under the RHR. EPA believes it is appropriate 

for the WRAP States to use the revised IMPROVE equation.

D. Baseline Visibility Conditions

EPA's "Guidance for Tracking Progress Under the Regional Haze Rule" ("GTP") describes a 

lengthy and detailed step-by-step process for calculating the visibility metric to be used in 

tracking progress, including that for the baseline period 2000-2004 (GTP, sec. 2). The steps 

involve assembly of daily species concentration data from the IMPROVE network, inclusion of 

substitutions for missing data; assessment of site data completeness; calculation of extinction via 

the IMPROVE equation and then the deciview Haze Index; calculation of average deciviews for 

the 20% best and 20% worst days; averaging these over the 5 year period. These steps are 

mostly straightforward and were handled by the IMPROVE monitoring program, with the results 

displayed on the WRAP technical websites. The IMPROVE program to track Regional Haze at 

110 sites across the country to represent the national total of 156 Class I areas is funded by EPA 

and operated by the IMPROVE Steering Committee. The Committee is responsible for the 

documentation of the IMPROVE data and the calculated RHR 5-year averages. The main 

purpose of the WRAP documentation was to identify the differences between EPA guidance and 

WRAP procedures, which involved substitution for missing data beyond those in the “GTP” 

procedures.

For data substitution, the EPA guidance describes two procedures. The first is the use of 

quarterly median concentrations. This is the median concentration from the quarter the data 

value is missing from, averaged with similar medians from the preceding four years, using only 

quarters with having at least 50% of the days available and no more than 10 consecutive missing 

days (GTP, "Step 3: Determine Quarterly Median Concentrations for Missing Variables", p.2-5). 

The second substitution involves using quarterly averages, as long as the substitution changes 
10

extinction values by less than 10% in 90% of the data (GRP, "Step 5 - Evaluate Feasibility of 

Substituting Average Values", pp. 2-7 - 2-8).

Completeness requirements are also given in the EPA guidance: to be complete and included in 

the progress calculation, a year must have data for 50% of the sampling days in every quarter, 

75% of the days for the year overall, and no more than 10 consecutive missing sampling days; 

the overall 5-year period must have at least 3 complete years of data (GTP, "Step 7 - Check Data 

Completeness", pp. 2-8 - 2-9).

In order to meet the completeness requirement for IMPROVE 13 sites that either did not have 3 

complete years, or that were missing the year 2002 (needed for applying modeled relative 

response factors or RRFs to predict future conditions), WRAP used two additional procedures, 

carbon substitution and donor sites (WRAP, 2007)10. Under carbon substitution, WRAP used 

hydrogen as a surrogate for missing carbon, based on the historical relationship between carbon 

and hydrogen. The relationship was a quarterly regression between organic carbon (OC) and the 

fraction of hydrogen assumed to be associated with organic carbon (measured hydrogen, less 

24% of the measured sulfur to reflect hydrogen in inorganic compounds such as ammonium 

sulfate). In turn, elemental carbon (EC) was estimated from a quarterly regression with OC.

A second substitution method was the use of donor sites. For missing species, correlations 

between the missing data site and nearby candidate sites were calculation, and then donor sites 

selected in consultation with the State. Despite these two additional substitution methods, data 

did not meet the completeness requirements for four sites in 2000, and one site in 2001. 

However, at all sites data was considered complete for 2002, and also met the overall 

completeness requirements for the five-year period 2000-2004.

Review Comment: The WRAP used IMPROVE program-calculated data which followed EPA 

guidance for estimating baseline visibility conditions, but did use two additional data 

substitution methods. EPA believes the additional WRAP methods are reasonable extensions to 

the EPA approach, appropriate for the WRAP States to use, especially in view of the importance 

of the year 2002 for projecting future visibility conditions via modeling.

E. Natural Visibility Conditions

EPA guidance set out a default procedure for estimating natural conditions, but also describes 

circumstances when States might want to use a more refined approach, such as to reduce 

uncertainty when baseline visibility is already near natural conditions, or when there is marked 

seasonality; these might be accomplished via alternative estimates of natural concentrations, or 

use of temporally varying estimates (GENV sec. 3.1 and 3.2).

A Natural Haze Levels II Committee ("NH2C") was established with participation of several 

Regional Planning Organizations (RPOs) that are responsible for regional haze planning. This 

committee recommended a refined approach for estimating natural visibility conditions, which 

 10 "WRAP IMPROVE Data Substitutions", April 3, 2007; 

web page: http://vista.cira.colostate.edu/TSS/Results/Monitoring.aspx 

direct link http://vista.cira.colostate.edu/docs/wrap/Monitoring/WRAP_Data_Substitution_Methods_April_2007.doc
11

was used by the WRAP (Inter-RPO Natural Haze Levels II Committee, 2006; hereafter “NH2C 

report”)

11. The natural conditions approach used the revised IMPROVE equation, so that 

progress between baseline conditions and natural conditions can be calculated on a consistent 

basis. In brief, the approach estimated the best and worst 20% day visibility using the 

distribution of measured actual concentration data to reflect temporal variation; however these 

data were scaled so that their annual average matched the Trijonis values in the EPA guidance.

As discussed by the NH2C (NH2C report, notes to slide 5), the EPA default approach assumed 

that extinction is lognormally distributed, as is the case for the distributions of concentration for 

many pollutants. Since the deciview Haze Index is based on the logarithm of extinction, this 

implies that deciviews are normally distributed. Also, empirically this assumption looks 

reasonable (GENV sec 2.6). However, since Rayleigh scattering is essentially constant for a 

given monitoring site, and is thus a constant term in the IMPROVE equation for extinction, its 

logarithm cannot be normally distributed. This is important for natural conditions, when the 

Rayleigh term will be a substantial fraction of the total extinction.

This constant Rayleigh term was also included in calculations of mean and standard deviation of 

deciviews for each site. On scatter plots of standard deviation against mean using data from 

multiple sites, this led to an apparent decrease of standard deviation as mean decreases. 

Extrapolating down to a mean equal to the annual deciviews derived from the Trijonis/NAPAP 

natural concentrations led to a corresponding natural standard deviation, which was 2 deciviews 

for Western sites. However, for the least hazy sites, this decrease with mean was mainly due to 

the Rayleigh term. So, including the latter distorted the estimate of standard deviation, which in 

turn distorted the estimates of the 10th and 90th percentiles (NH2C report, notes to slide 5).

A further assumption in the EPA approach was that the average of the normally distributed 

deciviews of the 20% best or worst days can be respectively represented by the 10th or 90th 

percentile. (These are 1.28 standard deviations below or above the mean, respectively.) 

Actually the 8th and 92nd are better estimators; but since the distribution is not normal, this 

criticism is less relevant (NH2C report, notes to slide 5).

To address these issues, the NH2C developed a refined approach (NH2C report, notes to slide 6; 

Copeland et al., 200812

 11 "Natural Haze Levels II: Application of the New IMPROVE Algorithm to Natural Species Concentrations 

Estimates; Final Report by the Natural Haze Levels II Committee to the RPO Monitoring/Data Analysis 

Workgroup", presentation at WRAP Attribution of Haze Workgroup Meeting, July 26-27, 2006, Denver, CO. web 

page: 

) that did not depend on any statistical assumptions. Instead, actual 

monitored concentrations were used as the basis for the best 20% and worst 20% deciview days. 

For each monitoring site and each chemical species, actual IMPROVE concentrations were 

scaled so that the annual average was equal to Trijonis/NAPAP natural concentrations of the 

EPA default approach. These "Trijonis-adjusted" concentrations reflect the observed relative 

temporal variation of each IMPROVE site, yet are consistent with the natural concentrations in 

the EPA guidance. For each day, the adjusted concentrations were used in the revised 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm direct link: 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/029_NaturalCondII/naturalhazelevelsIIreport.ppt 12

http://vista.cira.colostate.edu/improve/publications/graylit/032_NaturalCondIIpaper/Copeland_etal_NaturalConditio

nsII_Description.pdf
12

IMPROVE equation to calculate extinction, and converted to deciviews. The respective 

averages of the 20% best and the 20% worst days' deciviews was then calculated directly, 

without assumptions on the statistical distributions as were needed in the EPA approach to 

translate extrapolated standard deviations into percentiles. Sites with annual average 

concentrations already below the Trijonis levels were not scaled upward, as that would imply 

current visibility would worsen in the future despite emission reductions. This procedure is 

consistent with the refined "sample-period-by-sample-period basis" described in EPA guidance 

(GENV sec. 3.6).

The procedure used has several acknowledged limitations. One it shares with the EPA approach: 

each chemical species can have one of only two possible background concentrations, one for the 

East and the one for the West. Future efforts may provide for a larger number of geographic 

zones with differing concentrations. Further, natural conditions are a flux across the ~120 

IMPROVE sample days per year; the daily/seasonal variation will need to be addressed as well.

Two other limitations were discussed by the NH2C along with alternative procedures to address 

them. First, the split between small and large particles in the revised IMPROVE equation 

depends on a relationship to concentration that is based on today's conditions. With future 

emission reductions, concentrations will be lower, and the relationship may change. The NH2C 

decided to avoid the significant effort and time needed to modify and validate changes to the 

IMPROVE equation that this would entail. In addition, future lower concentrations will likely 

involve relatively more particles in the smaller sizes already accommodated in the revised 

IMPROVE equation (NH2C report, notes to slide 6). The performance of the IMPROVE 

equation will need to be assessed over time as emission reductions occur.

A second potential limitation is that the same approach is used for both natural- and 

anthropogenic-dominated species components; EPA guidance mentions the possibility of treating 

these separately (GENV sec. 3.4). For the most part the variations considered by the NH2C kept 

some or all assumed natural species components constant, e.g. carbon assumed to come from 

natural fires. However, these alternative assumptions were rejected as not valid for all sites, 

and/or requiring more site-specific analysis than time permitted or than was warranted given the 

overall uncertainties (NH2C report, notes to slide 6). This, too, is a potential area for future 

efforts.

Review Comment: EPA guidance allows for a more refined approach to estimating natural 

visibility conditions (GENVC 3.1, 3.3) as was used here. This is especially appropriate for areas 

in the West, some of which already have fairly good visibility and are affected episodically at a 

high magnitude by natural sources, e.g., wildfire. The method used by the WRAP matches EPA's 

general approach, but is more refined and does not rely on some of the statistical assumptions 

used in the default EPA procedure.

Review Comment: The approach used to estimate natural visibility seems superior to EPA 

default approach in a number of ways, and EPA believes it is appropriate for the WRAP States to 

use. 

3. Emissions Inventories
13

This WRAP emissions inventory work presented here was divided into three broad categories: 

point and area source projections developed by ERG, fire emissions inventory developed by Air 

Sciences and mobile source emissions inventories developed by ENVIRON. This chapter 

reviews the methodologies behind the WRAP analysis of each of these broad source categories 

to determine if the they are consistent with applicable EPA guidance and are therefore likely to 

be accurate, current and complete.

The overall emissions inventory work by the WRAP dealt with a very large array of sources and 

magnitudes varying across the West. Key anthropogenic source categories that must be 

addressed in the Regional Haze planning effort are discussed in this Chapter (e.g., point, 

stationary area, mobile, road dust, prescribed and agricultural fire). Discussion of sources that 

are defined as natural, quasi-natural, and/or have limited or no controls possible (e.g., wildfire, 

dust in general, ammonia, et cetera) and source categories mostly out of the control of the states 

(commercial marine, transport from other countries, et cetera) are also presented in a very limited 

fashion, prioritized to review of controls addressed in Regional Haze SIPs. Emissions from all 

of North America (other 4 RPO regions, Mexico, and Canada) were also used in the Regional 

Modeling Center scenarios (both historic and 2018 projections), but are not addressed in this 

section. Additional source categories (e.g., oil & gas area sources, biogenics, fugitive dust, 

windblown dust, et cetera) are not discussed.

A. Point and Area Sources Projections

The WRAP technical work for this category may be found in the Final Technical MemorandumFinal developed by ERG Titled “WRAP PRP18b Emissions Inventory – Revised Point and Area 

Sources Projections” revised October 16, 2009. The purpose of this evaluation is to determine if 

the WRAP Inventory meets the CAA requirement for a comprehensive, accurate and current 

inventory and can be used by States and Tribes for inclusion in their Regional Haze State 

Implementation Plans. The WRAP Inventory provides the most significant stationary point and 

area source visibility-impairing pollution within each of the following States: Alaska, Arizona, 

California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South 

Dakota, Utah, Washington, and Wyoming, plus tribal sources within these States. 

Scope of the Point and Area Source Projections

The Technical Memorandum identifies all stationary point and area sources located with the 

WRAP states of Alaska, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, 

North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming; as well as tribal sources 

within those states. Certain area sources are excluded because they are being dealt with by other 

contractor teams: (i.e., fugitive dust, fires, area ammonia sources, and on and off road mobile 

sources.). Updates to the oil and gas (O&G) area source emissions, and O&G point source SO2 

emissions were developed by ENVIRON under another project, and provided to ERG for 

inclusion with the other point and area sources in the WRAP PRP18 emissions inventory. The 

California emissions were not modified in any way, and were simply retained as-is from the 

previous WRAP 2018 base case version 1 inventory.
14

In some limited cases, changes were also made to the 2002 baseline inventory when these 

changes were straightforward and did not affect emission values (e.g., correction of missing or 

erroneous source classification codes [SCCs], changing facility names to be consistent across 

records, etc.). In only a few instances were records added to the 2002 inventory, and 

subsequently, were projected to 2018 and added to the WRAP PRP18 inventory. Additional 

2002 changes were identified, but were deferred until the next update of these 2002 and 2018 

WRAP inventories.

The following categories were NOT captured by the Technical Memorandum Developed by 

ERG: Fugitive dust, fires (natural, prescribed, and managed wildfire), area ammonia sources, and 

on and off road mobile sources.

Point and Area Source Methodology

ERG updated the existing WRAP 2018 version 1 database to be consistent with the 2018b “base 

case” emissions inventory used by the WRAP Regional Modeling Center (RMC) for the 2018 

base case modeling analysis completed in June 2006. These changes are itemized in the RMC 

2006 final report, Appendix A. After discussion with the RMC, and comparison to the 2018b 

base case emissions inventory, a few other changes were necessary to make the 2018 version 1 

inventory consistent with the 2018b base case inventory, including the following: 

• Added NOx and CO emissions for four new (post-2002) compressor stations in ND;

• Added SO2 emissions for 4 facilities in WY; and

• Added NOx, VOC, and CO emissions for 7 compressor stations in WY.

Using the updated 2018b base case emissions database, spreadsheets were developed to show the 

top 80% of emissions of point source and area source emissions, by SCC, for each state. Also, 

spreadsheet entitled “Proposed Changes & Feedback Request” itemized other potential changes 

that might need to be made to each jurisdiction’s 2018b base case inventory to update it for 

PRP18 purposes. The types of changes proposed and feedback requested from each state and 

local (S/L) agency included the following:

• Confirmation of changes in SO2 emissions from major sources located in §309 states (i.e., 

AZ, NM, UT, and WY);

• Request for BART emission limits;

• Request for information on any new post-2004 “on-the-books” controls or permit limits;

• Request to provide missing SCCs (and itemized list of records with missing SCCs was 

provided); and

• Confirmation that all dual-fueled sources were accounted for in the emissions inventory. 

These spreadsheets were e-mailed to each S/L agency for review prior to holding conference 

calls to explain the spreadsheets and receive initial comments from the agencies.

The basis of the WRAP 2002 baseline emissions inventory, and thus the WRAP 2018 version 1 

projections, were the data submitted by state and local (S/L) agencies and tribes for the U.S. EPA 

National Emissions Inventory (NEI). During the development of the NEI, ERG (as an U.S. EPA 

contractor) was requested to use NOx and SO2 emissions data for subject EGUs from the U.S. 
15

EPA Clean Air Markets Division (CAMD) database, instead of the emissions submitted by the 

S/L agencies for those EGUs reporting to CAMD. When these NOx and SO2 emissions data were 

placed into the NEI, they were assigned a unique facility ID beginning with “EGU” in order to 

distinguish that record from the record submitted by the state. Generally, the NOx and SO2

emissions values from CAMD varied only slightly from those submitted by the S/L agency (i.e., 

ERG observed results showing about a 1-2% difference in emissions between CAMD and S/L 

agency emissions). However, the use of the CAMD “EGU” numbering scheme caused there to 

be two different facility IDs for the same EGU in the resulting dataset. This eventually became 

recognized as a problem when these data were used in the WRAP 2002 and 2018 pivot tables 

and in the WRAP Emissions Data Management System (EDMS). These issues were corrected in 

the final memorandum. 

Review comments: EPA finds that the methodologies used to develop the stationary and area 

sources inventory is consistent with EPA’s guidance document titled: “Emissions Inventory 

Guidance for Implementation of Ozone and Particulate Matter national Ambient Air Quality 

Standards (NAAQS) and Regional Haze Regulations.” Dated August 2005.

B. Fire Emission Inventories

The technical work for this category may be found in the document: Development of 200-04 

Baseline period and 2018 Projection Year Emission Inventories, FINAL dated May 2007 by Air 

Sciences Inc. 

Scope and Methodology of the Fire Emission Inventories

The Fire Emissions Joint Forum (FEJF) of the Western Regional Air Partnership (WRAP) 

completed an air emission inventory for fire in 14 of the 15 states in the WRAP region. (Hawaii 

is not in the inventory.) The inventory includes emission estimates and activity data for wildfire,

prescribed fire, wildland fire use (WFU), agricultural burning, and non-federal prescribed

rangeland (NF rangeland) burning for the calendar year 2002. The FEJF collected fire activity

data from federal, tribal, and state agencies; arrived at data quality objectives; culled data from

the database that did not meet the data quality objectives; allocated summary data to realistic fire

events where necessary; devised emission calculation routines; estimated emissions for all fire

events; and published an emission inventory database and dispersion model-ready digital files.

Many of the techniques utilized for developing the fire emission inventory are based on the 

WRAP technical report entitled “1996 Fire Emission Inventory.” The FEJF produced an eventbased emission inventory, placing all fire emissions at coordinate locations on specific days.

Federal and state records of individual fire events were collected. For agricultural and nonfederal

rangeland burning, county level data on a monthly basis was collected and allocated to the 

coordinate level on a daily basis for jurisdictions where event level data was not available. In

general, burning activity data was not available directly from tribal agencies. Federal land

manager (especially Department of Interior – Bureau of Indian Affairs) data included in the

federal databases as well as state data may include burning in Indian Country. Quality Control

Packets where sent to states, Tribes, and Federal Land Management agencies (FLMs) to solicit

corrections to the 2002 activity data for wildland burning and agricultural burning.
16

Twelve pollutants are included in the fire inventory. They are: total suspended particulate matter 

(TSP), particulate matter less than 10 microns in diameter (PM10), PM2.5, elemental carbon (EC), 

organic carbon (OC), non-methane volatile organic compounds (VOC), methane (CH4), 

ammonia (NH3), oxides of nitrogen (NOx), carbon monoxide (CO), sulfur dioxide (SO2), and 

coarse particulate matter defined as the difference between PM10 and PM2.5 (PMcoarse). Activity 

records were used and checked for completeness for fire size, fuel loading, date, and location. 

Activity records deemed incomplete and therefore not useable in an emission calculation were 

culled from the database (and retained in a companion database for documentation purposes). 

Fuel loading and emission factor tables along with diurnal consumption and plume profiles were 

developed from the literature, expert and professional judgment, and stakeholder input. 

Spreadsheet and geographic information system software was used to store the data, perform

data augmentation and quality control functions, calculate emissions, and produce the strictly

formatted National Emission Inventory Format (NIF) 3.0 and SMOKE/IDA text export files of 

the inventories. The fire inventory was passed to the Regional Modeling Center as PM2.5, 

PM10, NOx and CO emissions, and the RMC applied chemical profiles to derive fully speciated 

and gridded fire emissions data. The fire inventory was provided to the RMC with a lookup 

table for effective plume rise based on acreage burned per day, and a lookup table for diurnal 

emission profile and corresponding smoldering at the end of the fire event. 

Limitations of the Fire Emission Inventory

Limitations of the fire emission inventory include the omission of fire events (e.g., tribal burning, 

in general, not accounted for in non-tribal data sets and individual fire activity records deemed to

be incomplete) and variable data quality due to the variety of data sources used. 

Estimating emissions from fire events involves considerable scientific uncertainty. Historic data

are of varying quality and for some areas unavailable. Activity records were not ground-truthed

and, other than quality control steps described in Air Sciences report, were generally accepted 

“as is.” Parameters such as the vegetation type of a burn, the vegetation-specific fuel loading, 

pollutant specific emission factors, and combustion efficiencies, to name a few, all have 

uncertainties associated with them which may influence emission estimates and regional 

modeling results. Fire has been traditionally treated as an “area” source, but in the WRAP’s 

regional dispersion model, it is treated as a point source. Therefore, fire emissions were placed 

at a latitude/longitude coordinate location for each day. From the daily and spatially resolved

emission inventory, hourly consumption and plume rise were estimated. Finally, the inventory 

states that professional judgment was used to select the best available or most representative 

parameters or methods to estimate emissions. However, other parameters and methods could 

have been chosen and could also be considered “reasonable” for estimating emissions from fire.

Review comments: EPA finds that the methodologies used to develop the fire sources inventory is 

consistent with EPA’s guidance document titled: “Emissions Inventory Guidance for 

Implementation of Ozone and Particulate Matter national Ambient Air Quality Standards 

(NAAQS) and Regional Haze Regulations.” Dated August 2005. 

C. Mobile Source Inventories
17

The technical work for these source categories may be found in the document titled: Final 

Report, WRAP Mobile Source Emission Inventories Update, Dated, May 2006, prepared by 

ENVIRON. The purpose of this evaluation is to determine if the methodologies used to develop 

the Mobile Source Emission Inventories are adequate for a Regional Haze SIP. 

Scope of the Mobile Source Inventories

ENVIRON has produced methodologies for the following mobile sources: On-Road, Off Road, 

Locomotive, Aircraft, Commercial Marine, and Road Dust. 

The scopes of these inventories are as follows:

Geographic domain: Emissions were estimated by county for all counties in the following 14 

states: Alaska, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North 

Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming. Hawaii was not included.

Temporal resolution: Emissions were estimated for an average day in each of the four seasons, 

and for an average annual weekday. Seasons are defined as three-month periods: spring is 

March through May; summer is June through August; fall is September through November; and 

winter is December through February. Emissions were estimated for the 2002 base year and for 

three future years – 2008, 2013, and 2018.

Pollutants: Emissions were estimated for primary Particulate Matter (PM10 and 2.5), Nitrogen 

Oxides (NOx), Sulfur Dioxide (SO2), Volatile Organic Compounds (VOC’s), Carbon Monoxide 

(CO), Ammonia (NH3), Elemental and Organic Carbon(EC/OC), and sulfate (SO4).

Sources: For all pollutants, emissions were estimated separately by vehicle class for on-road 

sources and by equipment type/engine type for off-road sources. Emissions were summarized 

for gasoline and diesel-fueled engines. 

Mobile Source Inventory Methodologies 

As with most emissions sources, on-road and off-road mobile source emissions are estimated as 

the products of emission factors and activity estimates. With the exception of California, all onroad mobile sources emission factors were derived from EPA’s MOBILE613 model. Activity for 

on-road mobile sources is vehicle miles traveled stated as VMT. State and local agencies were 

provided default modeling inputs and VMT levels for base and future years for review and 

update; all states and several agencies provided updated. The California Air Resources Board 

(CARB) provided on-road emissions estimates by county and vehicle class directly; these were 

based on CARB’s in-house version of their EMFAC model for the State of California.

 13 EPA’s MOBILE6 model is available at http://www.epa.gov/OMSWWW/m6.htm. 
18

For all states except California, EPA’s draft NONROAD2004 model was used to estimate socalled traditional off-road sources14, all sources listed above except aircraft, commercial marine, 

and locomotives. The NONROAD model includes estimates of emission factors, activity levels, 

and growth factors for all traditional off-road sources. The default activity levels were provided 

to state agencies for input and update; however, no state provided updated off-road activity data. 

Emissions estimation methods for aircraft, commercial marine, and locomotives were similar to 

approaches EPA has recently used in developing national emission inventories. For California, 

CARB provided off-road emissions estimates by source category and county directly.

Here are the methodologies used to create the mobile source inventories:

On-Road:

The emissions model inputs and calculations performed for estimating the 2002 base year and 

2008/2013/2018 future year on-road emissions used EPA’s MOBILE6 model. The generation of 

SMOKE files for input into the air quality model was also used. To estimate on-road emissions, 

defaults were established for all mobile source model input parameters, and also for VMT 

estimates. These default inputs were sent to state and local air quality planning agencies in the 

WRAP states (except for California), and they were requested to provide the most up-to-date 

modeling and VMT inputs. 

Updated data and model inputs provided by state and local contacts are shown below. All state 

agencies responded to the survey requests for base and future year modeling inputs. In addition, 

survey responses were received from the local agencies for the following counties:

• Arizona – Pima and Maricopa 

• Colorado – Denver nonattainment area counties

• Nevada – Clark and Washoe

• New Mexico – Bernalillo 

For California, the Air Resources Board provided emissions estimates directly for all years, 

estimated using an internal version of the EMFAC2002 model, with updated activity data for 

some areas. 

The default VMT data used as the starting point in this analysis were the annual VMT estimates 

that EPA had compiled for the 2002 National Emission Inventory (NEI2002), in the county 

database for the National Mobile Inventory Model15

 14 The final version of NONROAD (NONROAD2005, available at 

(NMIM). EPA had derived the VMT 

estimates from Highway Performance Monitoring System (HPMS) data from the Federal 

Highway Administration (FHWA). State and local data provided to EPA as part of the June 

2004 Consolidated Emissions Reporting Rule (CERR) submittals were incorporated into the 

default VMT estimates. These default VMT estimates, by county, vehicle type, and roadway 

type, were posted to the WRAP Mobile Sources Update Project web page, and a survey form 

http://www.epa.gov/otaq/nonrdmdl.htm) was 

released after the work in this project was completed. 

15 National Mobile Inventory Model is available at http://www.epa.gov/OMSWWW/nmim.htm. 
19

was sent to state and local air quality planners asking them to review the estimates and provide 

updates where available. By default the annual VMT were assumed to be allocated evenly to 

each season; two states (WA and UT) had provided seasonal VMT allocation data that was 

included in the default modeling inputs files posted. 

Most of the state and local agencies that responded to the survey provided updated VMT 

estimates. The areas for which updates were not provided and the state or local agency accepted 

the NMIM defaults were: Arizona - all counties except Maricopa; Denver area; North Dakota; 

South Dakota; and Washington. In most of these cases, the VMT estimates had been previously 

provided to EPA under the CERR and had already been incorporated into the NMIM database.

Off Road:

This section describes the methods for estimating 2002 base year and 2008/2013/2018 future 

year emissions for so-called traditional off-road equipment. Equipment types included here are 

in the following categories:

• airport ground support, such as terminal tractors;

• agricultural equipment, such as tractors, combines, and balers;

• construction equipment, such as graders and back hoes;

• industrial and commercial equipment, such as fork lifts and sweepers;

• recreational vehicles, such as all-terrain vehicles and off-road motorcycles; residential and 

commercial lawn and garden equipment, such as leaf and snow blowers; 

• logging equipment, such as shredders and large chain saws;

• recreational marine vessels, such as power boats;

• underground mining equipment; and

• oil field equipment.

Seasonal average daily emissions were estimated for the thirteen non-California western states 

using EPA’s NONROAD model. To estimate emissions from these sources, defaults were 

established for all NONROAD model input parameters. These default inputs were sent to state 

and local air quality planning agencies in the WRAP states (except for California), and they were 

requested to provide the most up-to-date modeling. For California, the Air Resources Board 

(CARB) provided all off-road emissions estimates from their own modeling system, which has 

similar equipment types. 

Locomotive:

County level locomotive emissions estimates were estimated as the product of locomotive fuel 

consumption and average locomotive emission factors. Previous WRAP locomotive emissions 

estimates (Pollack et al., 2004) allocated national fuel consumption estimates to counties using 

emissions data offered by the National Emissions Inventory. For this project a detailed revision 

to that allocation method was developed for allocating 2002 national fuel consumption estimates. 

Emission factors were also revised to combine line-haul and switching engines because only 

national total fuel consumption was available. Additional emission factors for ammonia and fuel 
20

sulfur provided by EPA were also incorporated and form the basis from which sulfur dioxide 

was estimated.

Aircraft:

County-level aircraft emissions for 2002 for the WRAP states were obtained from work 

performed for EPA’s 2002 National Emissions Inventory (NEI2002). Activity data for aircraft 

emissions are takeoff cycles (LTOs), and emission factors are primarily from the Federal 

Aviation Administration (FAA) Emissions and Dispersion Modeling System (EDMS). The 

2002 emissions were projected to future years using forecast LTOs available from the FAA. 

More detailed estimates were provided for some states.

The FAA EDMS model combines specified aircraft and activity levels with default emissions

factors in order to estimate annual inventories for a specific airport. Aircraft activity levels in 

EDMS are expressed in terms of LTOs, which consist of the four aircraft operating modes: taxi 

and queue, take-off, climb-out, and landing. Default values for the amount of time a specific 

aircraft spends in each mode, or the time-in-modes (TIMs), are coded into EDMS.

Aircraft emissions were estimated for four aircraft categories:

• Air carriers, which are larger turbine-powered commercial aircraft with at least 60 seats or 

18,000 lbs payload capacity;

• Air taxis, which are commercial turbine or piston-powered aircraft with less than 60 seats or 

18,000 lbs payload capacity; 

• General aviation aircraft, which are small piston-powered, non-commercial aircraft; and 

• Military aircraft. 

Commercial Marine:

Commercial marine emissions comprise a wide variety of vessel types and uses. Table 3.1 

describes the different types of commercial marine vessel activity. In the previous WRAP 

mobile sources emission inventory work, emissions were estimated for most types of vessels 

(Pollack et al., 200416). 

Table 3.1. Commercial Vessel Types.

Source Definition Purpose Geographic Area

Deep draft Ocean-going large 

vessels

Ocean Traffic

Near port

Tow or Push Boats Barge Freight River Traffic

Ocean Traffic

Tugs Vessel assist and 

support functions Near port

 16 Pollack, A.K., R. Chi, C. Lindhjem, C. Tran, P. Chandraker., P. Heirigs, L. Williams, S. S. Delaney, M. A. 

Mullen, and D. B. Thesing. 2004. “Development of WRAP MOBILE Source Emission Inventories.” Prepared for 

Western Governors’ Association, Denver, Colorado. 
21

Ferries River or lake 

ferrying Regular routes

Other Commercial 

Vessels

Smaller support or 

excursion boats Near dock

Dredges Dredging projects Varies

Commercial Fishing Market fishing Ocean

Military Coast Guard and 

Navy Ocean & Port

For this inventory, emissions were estimated for deep draft vessels within shore and near port 

using port call data, and offshore emissions generated from ship location data. The most 

important revision for commercial marine emissions leading to regional haze (PM, SOx, and 

NOx) was the estimation of emissions for the offshore activity, primarily of ocean-going vessels. 

This activity was not previously estimated for the WRAP emission inventory, and has been a 

subject of concern as vessel traffic passes out from and along and upwind of the western coast of 

the US. The other revision conducted here was to update in-shore deep-draft vessel emissions to 

reflect changing fleet mix, especially the retirement of steamship powered vessels.

One issue for modelers was which vertical grid layer to introduce the deep draft emissions. 

The stack height of 34 to 58 meters (Starcrest, 200417) and plume rise for ocean-going (deep 

draft) vessels indicated that the emissions should be placed in the second vertical layer (above 36 

and below 73 meters). The plume rise was estimated at 2 meters using standard plume rise 

models with the vessel speed of 17 to 25 knots as the wind speed, exhaust exit rate of 35 to 40

meters per second with an average stack diameter of about 1.3 meters18.

Road Dust:

In the previous WRAP mobile source emissions inventory work, fugitive road dust emissions for 

unpaved roads were revised from the traditional EPA estimates with updated silt loading values, 

updated and revised activity estimates, and the application of transport fractions (Pollack et al., 

2004). The aim of that work was to resolve large differences in road dust emissions in adjacent 

counties, and to use a consistent methodology across the WRAP region. The revised road dust 

emissions were estimated for 1996, the base year for the original WRAP modeling work, and 

2018. 

For this inventory, paved and unpaved road dust emissions were updated using the updated VMT 

for the base and future years provided by state and local contacts as part of the base and future 

year survey work. Any updated road dust controls provided were also incorporated into the 

estimates. 

Road dust emissions in the previous work included application of a factor to account for 

deposition and other removal mechanisms that tend to lower the amount of dust that is 

transported on a regional basis (i.e., across the 36 km grid cells in the WRAP modeling domain). 

The county-specific transport fractions that were applied depend on the vegetative characteristics 

 17 Port of Los Angeles. 2001 Baseline Emissions Inventory, prepared by Starcrest Consulting Group, LLC, June, 

2004.

18 http://vista.cira.colostate.edu/docs/wrap/emissions/OffshoreEmissions.doc
22

of each county, and were calculated as the weighted average of vegetation-specific transport 

fractions in each county. For the current work, updated transport fractions were available, but 

were applied to the road dust emissions (and other dust sources) in the SMOKE emissions 

processing rather than in the development of the county-level emissions.

Limitations of the Methodologies

Commercial Marine:

This inventory did not consider ocean-going vessels heading to and from Vancouver, B.C. and 

other Canadian traffic that may pass into or near US waters. The emissions from these vessels 

were estimated out 25 miles from the Pacific Ocean coast and used to compare with the estimates 

from the method used to estimate emissions off the coast and in the open ocean. For Alaska, 

there are missing ports that are not included in the inventory such as Juneau, the Alaska State 

capital. Juneau is locked out from motor vehicle access to the surrounding area and therefore 

relies upon marine shipping and transportation as well as air traffic for its goods and services. It 

is strongly advised that Alaska review the emissions carefully as there may be numerous 

omissions, Juneau being one of them. Military emissions were also not estimated because the 

activity data was not available to ENVIRON, and offshore emissions except those in the 

California emissions inventory were not included (i.e. off shore oil wells).

Road Dust Methodologies:

While this inventory was being developed, EPA’s guidance on estimating paved and unpaved 

road dust emissions was updated19. For this inventory, the road dust emissions only reflect 

updated VMT and controls supplied be State and local agencies, and do not reflect the updated 

EPA guidance methodology

Road dust emissions estimates in the earlier WRAP work did not include Alaska, as Alaska was 

not a WRAP member at the time. Therefore, road dust emissions are not estimated for Alaska. 

For California, road dust emissions provided by CARB were used.

Review Comments: The methodologies used to determine the 2002 base year three future years 

inventory – 2008, 2013, and 2018 are correct. EPA finds that the methodologies used to develop 

the mobile source inventory are consistent with EPA’s guidance with the above mentioned 

limitations.

D. Emissions Inventory Versions

WRAP developed several emissions inventories over the course of their technical work. This 

section describes only the final versions of these inventories that were used to develop the 2018 

visibility projections and one of the source apportionment analyses. The focus here is on the final 

emissions inventories served as the basis for technical work that was then incorporated into state 

regional have plans. 

 19 see http://www.epa.gov/ttn/chief/ap42/ch13/index.html. 
23

Two models were used in WRAP’s technical work. The Community Multiscale Air Quality 

Modeling System (CMAQ) was used to estimate visibility impairment at Class 1 areas in 2018. 

The Comprehensive Air Quality Model with extensions (CAMx) was used to quantify the 

relative contributions of sources of pollution causing visibility impairment at Class I areas in 

2018. These two efforts are described in more detail later in this document. 

Table 3.2 shows the final inventory versions used in these CAMx and CMAQ analyses. All of 

these inventories are complete, that is, they include all of the relevant point, area and mobile 

sources of pollution that have been shown to contribute visibility impairment. 

Table 3.2 – Inventories Used in WRAP Modeling

Purpose CMAQ Model CAMx Model

Model performance evaluation Base02b Base02b

2018 Visibility Projections Plan02d PRP18b

Particulate Source Apportionment Technology Plan02c Base18b

The first step in modeling is to evaluate the performance of the model to verify that it is 

representative of real world conditions. WRAP selected particular days where the ambient 

concentrations of pollutants at Class I areas are known and evaluated how well the model 

predicts those concentrations. One of the inputs to this model performance evaluation is an 

emissions inventory that accurately reflects actual emissions on the days in question. The WRAP 

used the Base02b inventory to evaluate the performance of both models.

The Plan02 inventories are designed to be representative of baseline conditions before the 

Regional Haze plans are put into place. They are different from the Base02 inventory in that 

multi-year averages are used for some source categories that are highly variable from year to 

year (e.g. wildfires and electric generating unit emissions). Additional refinements and data 

corrections were incorporated into the Plan02 inventories as they evolved in order to ensure the 

most accurate, representative 2002 inventories possible at the time of the modeling. Details on 

the inventories up until the Plan02c may be found in the WRAP summary document.20 The 

differences between the Plan02c and Plan02d inventories may be found in the WRAP Regional 

Modeling Center specification for Plan02d21. 

The two 2018 inventories used by WRAP in their final work are the Base18b inventory used in 

the particulate source apportionment technology (PSAT) analysis and the PRP18b model used to 

estimate 2018 visibility levels at Class I areas. The differences between the PRP18b and Base18b 

are discussed previously in this chapter. They are further detailed in the WRAP Regional 

Modeling Center specification for PRP18b22. 

Review Comment: It is reasonable and appropriate for WRAP to develop the Plan02 inventories 

to better represent baseline conditions and to incorporate the latest data into their emissions 

inventories.

 20 “Emissions Modeling” at http://vista.cira.colostate.edu/docs/wrap/emissions/EmissionsOverview.doc. 21 Plan02d specification at http://pah.cert.ucr.edu/aqm/308/spec_sheets/SpecSheet_Plan02d_11_03_2008.doc 22 Prb18b specification at http://pah.cert.ucr.edu/aqm/308/spec_sheets/SpecSheet_PRP18b_Aug11_2009final.doc
24

The different versions of the 2002 and 2018 inventories used in the PSAT and 2018 visibility 

projections are not significant for the WRAP area as a whole. In the future, it would be better to 

use identical inventories so that there is no perceived inconsistency. 

These emissions inventories are sufficiently complete, current and accurate for the purposes of 

inclusion in state regional haze plans.

4. Meteorological Modeling

Photochemical grid models, such as the Community Multiscale Air Quality Modeling System 

(CMAQ) and the Comprehensive Air quality Model with extensions (CAMx), require inputs of 

three-dimensional gridded meteorological data, including wind, temperature, humidity, 

cloud/precipitation, and boundary layer parameters. The Fifth-Generation Penn State/NCAR 

Mesoscale Model (MM5) was used to develop these input fields for the WRAP visibility 

modeling. MM5 is a state-of-the-science atmosphere model that has proven useful for air quality 

applications and has been used extensively in past local, state, regional, and national modeling 

efforts. MM5 has undergone extensive peer-review, with all of its components continually 

undergoing development and scrutiny by the modeling community. In-depth descriptions of 

MM523 can be found in Dudhia (1993)24 and Grell et al. (1994)25. The MM5 model is no longer 

being updated because new research efforts are focused on the Weather Research Forecast

(WRF) model, however, MM5 was the state of the science model at the time the WRAP 

visibility modeling was completed, and MM5 continues to be used in air quality modeling 

studies. All meteorological data used for the WRAP air quality modeling efforts are derived 

from MM5 model simulations. 

The SMOKE emissions processor also requires meteorological inputs derived from MM5, most 

notably for temperatures needed for mobile and biogenic emissions processing. MM5 derived 

windfields were also used in the creation of emission inventories for wind-driven emission 

sources.

The WRAP formed the Regional Modeling Center (RMC), consisting of the University of 

California at Riverside (UCR), ENVIRON International Corporation, and the University of 

North Carolina (UNC). The RMC completed all MM5 modeling necessary to support analysis 

for the Section 308 SIPs/TIPs. Initially, the RMC completed meteorology modeling for the 

entire year of 2002 on two grids: a continental-scale domain with 36-km grid spacing, and a 

regional-scale domain with 12 km grid spacing covering the western Class I areas. After 

concluding that visibility modeling had acceptable performance at both grid resolutions, the 12-

km modeling was discontinued. Therefore, this document only discusses the development of 

meteorological inputs for the 36-km domain. 

 23 http://www.mmm.ucar.edu/mm5 24 Dudhia, J., 1993. “A non-hydrostatic version of the Penn State/NCAR Mesoscale Model: validation tests and 

simulation of an Atlantic cyclone and cold front.” Mon. Wea. Rev. 121, pp.1493-1513. 25 Grell, G.A., J. Dudhia, and D.R. Stauffer, 1994. “A description of the Fifth Generation Penn State/NCAR 

Mesoscale Model (MM5).” NCAR Technical Note, NCAR TN-398-STR, 138 pp.
25

In addition to development of meteorological inputs for CMAQ and CAMx, MM5 was also used 

to develop meteorological inputs for the CALMET/CALPUFF modeling system. As discussed 

further in Section 7 of this TSD, CALMET/CALPUFF was used to determine whether a BART 

eligible source contributes to visibility impairment at a Class I area. Refer to Section 7 of this 

TSD for further information on the use of MM5 for BART modeling.

The WRAP meteorological modeling is described fully in the RMC report entitled Annual 2002 

MM5 Meteorological Modeling to Support Regional Haze Modeling of the Western United States

(Kemball-Cook et al., 2005)26.

A. Meteorological Modeling Protocol

The Revised Draft Protocol: 2002 Annual MM5 Simulations to Support WRAP CMAQ Visibility 

Modeling for the Section 308 SIP/TIP (ENVIRON and UCR, 2004)27 describes in detail the 

MM5 model and the setup and evaluation methods used by the RMC in the 2002 modeling 

effort. The Modeling Protocol provides a brief description of MM5, the WRAP modeling 

domain, the MM5 physical configuration, and model application. The Modeling Protocol also 

presents the plan for evaluating the performance of the model in replicating the evolution of 

observed winds, temperature, humidity, and boundary layer morphology. The model 

performance evaluation serves as the primary approach to assess the reliability of the 

meteorological fields to adequately characterize the state of the atmosphere for input to 

CMAQ/CAMx. Key elements of the MM5 model configuration and evaluation are discussed 

briefly below.

B. Meteorological Modeling Domain and Vertical Layer Structure

In the WRAP 36-km simulation, MM5 was configured to run on the standard continental-scale 

Regional Planning Organization (RPO) National Grid with 36-km grid point spacing (Figure 

4.1). The RPO National Grid is defined on a Lambert conformal projection, with true latitudes at 

33°N and 45°N, and the central latitude and longitude at 40°N and 97°W, respectively. The grid 

point spacing is 36-km. The continental expanse of this domain results in a grid of 165 (eastwest) by 129 (north-south) dot points, and 164 (east-west) by 128 (north-south) cross points. 

Overall, the domain covers 5904 km by 4608 km. The MM5 domain provides overlap of the 

CMAQ or CAMx air quality modeling grid to alleviate any numerical boundary artifacts that 

may be present in the MM5 output fields.

The vertical layer structure of the WRAP domain consists of 34 layers, a top level at 100 

millibars, and increasing layer thickness with altitude. The vertical layer structure is further 

detailed in the Modeling Protocol.

 26 Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2005. Annual 2002 MM5 

Meteorological Modeling to Support Regional Haze Modeling of the Western United States.” Draft Final Report. 

ENVIRON International Corporation and UC Riverside. Available at: http://pah.cert.ucr.edu/aqm/308/mm5.shtml 27 ENVIRON and UCR. 2004. “2002 Annual MM5 Simulations to Support WRAP CMAQ Visibility Modeling for 

the Section 308 SIP/TIP.” Draft Protocol. ENVIRON International Corporation and the University of California at 

Riverside. April. Available at: http://pah.cert.ucr.edu/aqm/308/mm5.shtml
26

Figure 4.1. RPO National 36-km MM5 Grid

C. Model Configuration

The final WRAP MM5 modeling system configuration for the 2002 annual simulation is 

provided in the Modeling Protocol. RMC conducted an initial simulation with a configuration 

taken from earlier MM5 simulations by the State of Iowa and LADCO. Then, further sensitivity 

tests were made to identify an MM5 configuration that would address performance issues 

identified from the initial annual run.

The initial 2002 36-km WRAP simulation results showed that MM5 performed better in the 

Central and Eastern U.S. than in the West, and performed generally better in winter than in 

summer (Kemball-Cook et al., 2005)26. In the western U.S., the amplitude of the diurnal 

temperature cycle was persistently underestimated during the summer, especially in the 

southwest. In the desert southwest, the humidity was greatly overestimated during the summer as 

well, and there was a pronounced cold bias. Some of these problems appeared to be linked to the 

excessive simulated precipitation generated by MM5 during the summer, especially in the 

southwest. This can have serious repercussions for CMAQ modeling since too much rain can 

“wash out” pollutants, while the too cool, humid and cloudy environment may lead to incorrect 

pollutant chemistry and aerosol thermodynamics. Wind performance improved quickly with 

height above the surface, suggesting that regional transport speeds/directions were reasonably 

represented. 
27

A number of sensitivity tests were performed by the RMC to resolve the poor performance issues 

identified in the initial simulation. In particular, RMC performed sensitivity tests related to four 

dimensional data assimilation (i.e., grid or analysis nudging), as well as for a number of physics 

options associated with cumulus parameterization, cloud microphysics, land surface models, and 

planetary boundary layer models. 

The result of sensitivity tests was a revised MM5 model configuration that showed:

• A dramatic reduction in the summertime cold, wet bias in the desert southwest;

• Surface temperature and humidity performance now within benchmarks for all WRAP 

subdomains except desert southwest for temperature;

• More accurate representation of the diurnal temperature cycle in the desert southwest;

• Improvements in the index of agreement for temperature, humidity and wind speed for all 

WRAP subdomains;

• A more realistic precipitation pattern over the western U.S.;

• Better model performance in the eastern U.S.; and

• An improvement in performance throughout the year, making it unnecessary to select 

different physics schemes for different seasons.

A full discussion of MM5 sensitivity testing and optimization can be found in Kemball-Cook et 

al., (2004)

28.

D. Model Performance

The model performance of MM5 in the 36-km simulations is described by the final RMC 

modeling report (Kemball-Cook et al., 2005)26. The goal of the evaluation was to determine 

whether the meteorological fields are sufficiently accurate to characterize properly the transport, 

chemistry, and removal processes in CMAQ. If errors in the meteorological fields are too large, 

the ability of the air quality model to replicate regional pollutant levels over the entire base year 

will be reduced and the predicted impacts from future year growth and controls will have greater 

uncertainty. To provide a reasonable meteorological characterization to the 

photochemical/visibility model, MM5 must represent with some fidelity the:

• Large-scale weather patterns (i.e., synoptic patterns depicted in the 850-300 mb height 

fields), as these are key forcings for mesoscale circulations;

• Mesoscale and regional wind, temperature, PBL height, humidity, and cloud/precipitation 

patterns;

• Mesoscale circulations such as sea breezes and mountain/drainage circulations;

• Diurnal cycles in PBL depth, temperature, and humidity.

For visibility applications, the moisture and condensate fields are particularly important as they 

significantly impact PM chemical formation and removal by wet and dry deposition. In addition, 

 28 Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2004. “MM5 Sensitivity Simulations 

to Identify a More Optimal MM5 Configuration Sensitivity Testing.” Revised Report. ENVIRON International 

Corporation and UC Riverside. Available at: http://pah.cert.ucr.edu/aqm/308/mm5.shtml
28

cloud and precipitation fields are a good measure of the integrated performance of the model 

since these are model-derived quantities and not nudged to observations. Because of the model’s 

coarse resolution of 36-km, the model cannot be expected to faithfully simulate the pattern or 

variability of the convective precipitation, but should reproduce the synoptic precipitation and 

cloud patterns.

The RMC performed a comprehensive model performance evaluation for the 2002 MM5 

simulation. This included a operational evaluation and a diagnostic evaluation using sensitivity 

simulation to optimize model performance for the western US. An operational evaluation entails 

an assessment of the model's ability to correctly estimate surface and boundary layer wind, 

temperature, and moisture largely independent of whether the actual process descriptions in the 

model are accurate. The operational evaluation essentially tests whether the predicted 

meteorological fields are reasonable, consistent, and agree adequately with available 

observations in time and space.

The basis for the RMC operational performance assessment entailed a comparison of the 

predicted meteorological fields to available surface and aloft data that are collected, analyzed, 

and disseminated by the National Weather Service. It was carried out both graphically and 

statistically to evaluate model performance for winds, temperatures, humidity, and the 

placement, intensity, and evolution of key weather phenomena. The MM5 results were 

compared to a specific set of statistics that have been identified for use in establishing 

benchmarks for acceptable MM5 model performance (Emery et al., 2001)29.

The RMC concluded, based on the results of the performance evaluation, that the final 36 km 

WRAP MM5 simulations exhibit reasonably good performance that is within the bounds of other 

meteorological databases used for prior air quality modeling efforts. It was therefore deemed 

reasonable to proceed with its use as inputs for visibility modeling.

Review Comment: The MM5 meteorological model used by WRAP was state-of-the-science at 

the time the modeling was conducted and the performance of the model was adequate for the 

purposes for which it was used and on par with other studies at the time (Emery et al., 2001). 

E. MM5 Processing and Application

Several preprocessing steps are necessary to prepare input data for an MM5 simulation. The 

MM5 modeling system provides all of the tools necessary to prepare topographic, vegetative, 

initial condition, boundary condition, and FDDA nudging input files.

Global topographic data at 10-minute (latitude/longitude) resolution were used to define terrain 

elevations on the 36-km grid. Land use distribution on the MM5 domains was defined from the 

24-category USGS vegetation data with a resolution of 10 minutes.

 29 Emery, C.A. and E. Tai. 2001. “Enhanced meteorological modeling and performance evaluation for two Texas 

ozone episodes.” Prepared for the Texas Natural Resource Conservation Commission, by ENVIRON International 

Corporation.
29

The underlying objective analyses used for the 2002 WRAP simulation were taken from the Eta 

Data Assimilation System (EDAS). EDAS is available from NCAR, and contains 3-hourly 

objective analysis initialization and forecast fields from the National Center for Environmental 

Prediction’s (NCEP) ETA model, which is the current short-term national operational forecasting 

platform. The EDAS analyses are provided on a standardized continental-scale pressure-level 

Lambert grid with ~40 km horizontal grid point spacing. The EDAS analyses are developed from 

a wide variety of observational sources, including standard surface and upper air measurements, 

profiler networks, radar- and satellite-derived measurements, and ship and aircraft reports. The 

wide array of data sources, coupled with the high time and spatial resolution provided by EDAS, 

result in an analysis product that far exceeds the level of detail found in traditional global-scale 

analyses.

The EDAS analyses were processed for use by MM5 as initial/boundary conditions, and for 

analysis nudging in the FDDA package. In addition, the raw EDAS was enhanced for nonstandard pressure levels. Sea surface temperatures (SSTs) were approximated by ETA skin 

temperatures, and the SSTs were allowed to vary over the course of the simulation. In addition, 

the NCEP ADP Global Surface Observations and the NCEP ADP Upper Air Observations were 

used to develop the 2-D FDDA fields for use in MM5.

The annual simulation was made in sequential 5-day run segments, each with an initial spinup 

period of 12 hours that overlapped the last 12 hours of the preceding run. The model was spun 

up for the final two weeks of December 2001 to allow for photochemical/visibility applications 

with start dates at the beginning of January 2002.30

5. Visibility Modeling

The WRAP has created a document titled Air Quality Modeling31 that describes the visibility 

modeling tools that they used to develop technical products in support of SIP development. 

Excerpts of WRAP’s Air Quality Modeling document referenced here were drafted prior to the 

development of the final base case planning emissions inventory called ‘Plan02d’, and prior to 

the development of the final 2018 future-year emissions inventory called ‘PRP18b’. However, 

the excerpts of Air Quality Modeling below remain applicable to those later emissions scenarios, 

and where appropriate, EPA has added explanatory as well as review comments. 

Air Quality Models

The WRAP RMC utilized two regulatory air quality modeling systems to conduct all regional 

haze modeling. A brief discussion of each of these models is provided below.

Community Multi-Scale Air Quality Model 

 30 A list of the run segments and their date/time durations is provided at 

http://pah.cert.ucr.edu/aqm/308/2002met/mm5/2002_MM5_dateskey_UCR.xls

31 http://vista.cira.colostate.edu/docs/wrap/Modeling/AirQualityModeling.doc
30

EPA initially developed the Community Multi-Scale Air Quality (CMAQ) modeling system in 

the late 1990s. The model source code and supporting data can be downloaded from the 

Community Modeling and Analysis System (CMAS) Center32, which is funded by EPA to 

distribute and provide limited support for CMAQ users. CMAQ was designed as a “one 

atmosphere” modeling system to encompass modeling of multiple pollutants and issues, 

including ozone, PM, visibility, and air toxics. This is in contrast to many earlier air quality 

models that focused on single-pollutant issues (e.g., ozone modeling by the Urban Airshed 

Model). CMAQ is an Eulerian model—that is, it is a grid-based model in which the frame of 

reference is a fixed, three-dimensional (3-D) grid with uniformly sized horizontal grid cells and 

variable vertical layer thicknesses. The number and size of grid cells and the number and 

thicknesses of layers are defined by the user, based in part on the size of the modeling domain to 

be used for each modeling project. The key science processes included in CMAQ are emissions, 

advection and dispersion, photochemical transformation, aerosol thermodynamics and phase 

transfer, aqueous chemistry, and wet and dry deposition of trace species. CMAQ offers a variety 

of choices in the numerical algorithms for treating many of these processes, and it is designed so 

that new algorithms can be included in the model. CMAQ offers a choice of three photochemical 

mechanisms for solving gas-phase chemistry: the Regional Acid Deposition Mechanism version 

2 (RADM2), a fixed coefficient version of the SAPRC90 mechanism, and the Carbon Bond IV 

mechanism (CB-IV). 

Comprehensive Air Quality Model with Extensions 

The Comprehensive Air Quality Model with extensions (CAMx) model was initially developed 

by ENVIRON in the late 1990s as a nested-grid, gas-phase, Eulerian photochemical grid model. 

ENVIRON later revised CAMx to treat PM, visibility, and air toxics. While there are many 

similarities between the CMAQ and CAMx systems, there are also some significant differences 

in their treatment of advection, dispersion, aerosol formation, and dry and wet deposition.

Explanatory Comment: WRAP used CMAQ modeling for model performance evaluations with 

the Base02b emissions inventory, and then used CMAQ with Plan02d and PRP18b emissions 

inventories to generate the relative response factors (RRF) needed to project 2018 future-year 

visibility. WRAP used CAMx modeling for model performance evaluations with the Base02b 

emissions inventory, and then used CAMx with its Particulate Source Apportionment Technology 

(PSAT) tool to provide source apportionment of nitrate and sulfate aerosol with both the 

Plan02c and Base18b emissions inventories. 

Review Comment: States and Tribes are likely to rely heavily on the CAMx PSAT results to 

quantify the sources of nitrate and sulfate impacting their Class I Areas and, therefore, to shape 

their approach to emissions controls. However, WRAP has provided visibility projections for 

2018 using the CMAQ model, not CAMx. WRAP compared the model performance of CMAQ 

and CAMx using the Base02b emissions inventory and found them comparable, but not identical. 

While WRAP’s use of different models for different products in this case is not considered 

significant, it is advised that in the future there be better alignment in the choice of models so 

that there is no perceived inconsistency. 

 32 http://www.cmascenter.org/
31

Model Versions

Both EPA and ENVIRON periodically update and revise their models as new science or other 

improvements to the models are developed. WRAP elected to operate CMAQ v4.5 using the 

MM5 data processed using MCIP v2.3 and the AE3 aerosol module. The version used for the 

comparison of CMAQ and CAMx was CAMx v4.3. 

Review Comment: The versions of CMAQ and CAMx used by WRAP in its visibility modeling 

were the state-of-the-science at the time they were implemented. 

Major differences between the two models that still exist are in the basic model code, in the 

treatment of horizontal diffusion SOA formation mechanisms, and in grid nesting. The publicly 

released version of CAMx supports ozone and PM source apportionment through its Ozone and 

PM Source Apportionment Technology (OSAT/PSAT) probing tools. 

Model Simulations

In support of the WRAP Regional Haze air quality modeling efforts, the RMC developed air 

quality modeling inputs including annual meteorology and emissions inventories for a 2002 

actual emissions base case, a planning case to represent the 2000-04 regional haze baseline 

period using averages for key emissions categories, and a 2018 base case of projected emissions. 

All emission inventories were developed using the Sparse Matrix Operator Kernel Emissions 

(SMOKE) modeling system. Each of these inventories has undergone a number of revisions 

throughout the development process to arrive at the final versions used in CMAQ and CAMx air 

quality modeling. The development of each of these emission scenarios is as follows: 

• The 2002 base case emissions scenario, referred to as “2002 Base Case” or “Base02”. The 

purpose of the Base02 inventory is to represent the actual conditions in calendar year 2002 

with respect to ambient air quality and the associated sources of criteria and particulate 

matter air pollutants. The Base02 emissions inventories are used to evaluate the air quality 

model and associated databases and to demonstrate acceptable model performance with 

respect to replicating observed particulate matter air quality. 

Explanatory Comment: The final base case emissions inventory used was called ‘Base02b’. For 

a review of this emissions inventory see the emissions inventory development section of this 

document. 

• The 2000-04 baseline period planning case emissions scenario is referred to as “Plan02”. The 

purpose of the Plan02 inventory is to represent baseline emission patterns based on average, 

or “typical”, conditions. This inventory provides a basis for comparison with the future year 

2018 projected emissions, as well as to gauge reasonable progress with respect to future year 

visibility.

 

Explanatory Comment: The final baseline planning case used was called ‘Plan02d’. For a 

review of this emissions inventory see the emissions inventory development section of this 

document.
32

• The 2018 future-year base case emissions scenario, referred to as “2018 Base Case” or 

“Base18”. These emissions are used to represent conditions in future year 2018 with respect 

to sources of criteria and particulate matter air pollutants, taking into consideration growth 

and controls. Modeling results based on this emission inventory are used to define the future 

year ambient air quality and visibility metrics. 

Explanatory Comment: The final future-year scenario used was called ‘PRP18b’. For a review 

of this emissions inventory see the emissions inventory development section of this document. 

Data Sources

The CMAQ model requires inputs of three-dimensional gridded wind, temperature, humidity, 

cloud/precipitation, and boundary layer parameters. All meteorological data used for the WRAP 

air quality modeling efforts are derived from MM5 model simulations. 

Review Comment: Meteorological modeling is reviewed elsewhere in this document.

Review Comment: WRAP conducted a sensitivity simulations comparing model performance at 

36 and 12 km horizontal resolution and found no appreciable performance improvement in the 

model outputs at 12 km resolution over those at 36 km resolution. At the time, given resource 

constraints and the lack of improved performance, WRAP decided to conduct all emissions, 

meteorological, and visibility modeling at 36 km horizontal resolution. WRAP’s choice of 36 km 

horizontal resolution was appropriate given the computer and resource limitations and the lack 

of improved performance at 12 km resolution. 

Emission inventories for all WRAP air quality simulations were developed using the Sparse

Matrix Operator Kernel Emissions (SMOKE) modeling system. 

Initial conditions (ICs) are specified by the user for the first day of a model simulation. For 

continental-scale modeling using the RPO Unified 36-km domain, the ICs can affect model 

results for as many as 15 days, although the effect typically becomes very small after about 7 

days. A model spin-up period is included in each simulation to eliminate any effects from the 

ICs. For the WRAP modeling, the annual simulation is divided into four quarters, and included a 

15-day spin-up period for the quarters beginning in April, July, and October. For the quarter 

beginning in January 2002, a spin-up period covering December 16-31, 2001, using meteorology 

and emissions data developed for CENRAP were used.

Review Comment: The 15 day spin-up period employed by WRAP was sufficient given the size of 

the modeling domain. 

Boundary conditions (BCs) specify the concentrations of gas and PM species at the four lateral 

boundaries of the model domain. BCs determine the amounts of gas and PM species that are 

transported into the model domain when winds flow is into the domain. Boundary conditions 

have a much larger effect on model simulations than do ICs. For some areas in the WRAP region 

and for clean conditions, the BCs can be a substantial contributor to visibility impairment. For 
33

this study BC data generated in an annual simulation of the global-scale GEOS-CHEM model 

that was completed by Jacob et al. (http://acmg.seas.harvard.edu/geos/) for calendar year 2002 

were applied. 

Review Comment: Boundary conditions employed by WRAP were state-of-the-science at the 

time they were implemented. 

The CMAQ model options and configuration used for the WRAP 36-km model simulations are 

described in Tonnesen et al., 200633.

2002 Base Case Modeling

The purpose of the 2002 Base Case modeling efforts was to evaluate air quality/visibility 

modeling systems for a historical episode—in this case, for calendar year 2002—to demonstrate 

the suitability of the modeling systems for subsequent planning, sensitivity, and emissions 

control strategy modeling. Model performance evaluation is performed by comparing output 

from model simulations with ambient air quality data for the same time period. 

Model Performance Evaluation

The objective of a model performance evaluation (MPE) is to compare model-simulated 

concentrations with observed data to determine whether the model’s performance is sufficiently 

accurate to justify using the model for simulating future conditions. There are a number of 

challenges in completing an annual MPE for regional haze. The model must be compared to 

ambient data from several different monitoring networks for both PM and gaseous species, for an 

annual time period, and for a large number of sites. The model must be evaluated for both the 

worst visibility conditions and for very clean conditions. Finally, final guidance on how to 

perform an MPE for fine-particulate models was not available from EPA when WRAP 

completed its modeling. Therefore, the RMC experimented with many different approaches for 

showing model performance results. The plot types that were found to be the most useful are the 

following:

• Time-series plots comparing the measured and model-predicted species concentrations

• Scatter plots showing model predictions on the y-axis and ambient data on the x-axis

• Spatial analysis plots with ambient data overlaid on model predictions

• Bar plots comparing the mean fractional bias (MFB) or mean fractional error (MFE) 

performance metrics 

• “Bugle plots” showing how model performance varies as a function of the PM species 

concentration

• Stacked-bar plots of contributions to light extinction for the average of the best-20% 

visibility days or the worst-20% visibility days at each site; the higher the light extinction, the 

lower the visibility

 33 http://pah.cert.ucr.edu/aqm/308/reports/final/2002_MPE_report_main_body_FINAL.pdf
34

Explanatory Comment: The following plots depict summary model performance for WRAP 2002 

Base Case modeling using the Base02b emissions inventory and were downloaded from the RMC 

web site. Below are three sets of model bias and model error plots. Each set of plots compares 

the measured chemically speciated aerosol data from a monitoring network with the 

corresponding model output. The monitoring networks used for comparison are IMPROVE, 

CASTNET, and STN, and are treated separately because each monitoring network has different 

goals, siting criteria, and data collection protocols. The model performance plots depicted here 

are “bugle plots”, and depict model performance (symbols) and model performance standards 

(curves) on the y axis relative to measured concentration on the x axis. Model performance 

standards are of greater latitude at lower concentrations because of the higher relative 

uncertainties in the data at lower concentrations. There are twelve symbols for each chemical 

species, which represents the monthly model performance for 2002. Model performance at 

IMPROVE monitors is of highest importance, because these monitors are sited to be 

representative of the visibility conditions impacting each Class I Area. The CASTNET 

monitoring network is more sparse than the IMPROVE network, but is also mostly sited at Class 

I Areas and as such, model performance at CASTNET sites should also be considered important. 

The STN monitoring network is an urban network, and model performance relative to this 

network should be given less importance. 
35

Figure 5.1. WRAP model performance (factional bias and error) of the Base02b modeling 

scenario for chemically speciated aerosol data from the IMPROVE monitoring network. The 12 

symbols for each chemical species represent monthly average model performance for the year 

2002, averaging all monitors in the WRAP region. Solid lines represent WRAP modeling goals 

and criteria. For chemical species acronym explanations, see text below. 
36

Figure 5.2. WRAP model performance (factional bias and error) of the Base02b modeling 

scenario for chemically speciated aerosol data from the CASTNET monitoring network. The 12 

symbols for each chemical species represent monthly average model performance for the year 

2002, averaging all monitors in the WRAP region. Solid lines represent WRAP modeling goals 

and criteria. For chemical species acronym explanations, see text below.
37

Figure 5.3. WRAP model performance (factional bias and error) of the Base02b modeling 

scenario for chemically speciated aerosol data from the STN monitoring network. The 12 

symbols for each chemical species represent monthly average model performance for the year 

2002, averaging all monitors in the WRAP region. Solid lines represent WRAP modeling goals 

and criteria. For chemical species acronym explanations, see text below.
38

Review Comment: The model performance goals and criteria used by WRAP were appropriate 

at the time the modeling was conducted. 

Review Comment: For IMPROVE and CASTNET monitoring sites, model performance was 

adequate based on stated criteria except for course particulate matter (CM). WRAP addressed 

this model performance issue by setting all CM relative response factors (RRF) to a value of 1 

when projecting course matter visibility impacts to the future year 2018, regardless of the future 

year modeling results. This is considered acceptable for most sites because CM that is both 

anthropogenic and controllable is not believed to be of major importance for visibility 

impairment at most Class I Areas. However, where anthropogenic course PM is suspected of 

being a significant contributor, SIP developers should not rely on WRAP CMAQ model 

projections and should provide alternate technical analyses. 

Review Comment: For STN monitoring sites, WRAP modeling showed significant 

underpredictions for all species and was beyond the criteria for nitrate and organic carbon. 

However, these model underpredictions are expected given that WRAP’s model resolution was 

36 km and urban areas are known to have significantly inhomogeneous PM concentrations over 

a scale of 36 km. Typically, all model emissions are immediately diluted into the grid box they 

are assigned to. Hence, monitors that experience emissions at time scales that don’t allow for 

the dilution that is assumed in the model are likely to show model underpredictions. 

Review Comment: The above model performance summary includes all sites within the WRAP. 

However, a model performance summary over such a diverse geographic area may mask model 

performance issues occurring in smaller geographic sub-regions. WRAP performed some 

limited experiments analyzing model performance on a sub-regional basis, however, WRAP 

technical staff never arrived at an agreed upon methodology to define the sub-regions. There 

were many factors to consider when defining sub-regions for model performance, some of which 

were conflicting. Additionally, including sub-regional model performance analysis was 

considered resource prohibitive at the time. In future Regional Haze modeling and analyses, it 

is recommended that more work be done to define coherent sub-regions for model performance 

analysis so that this issue is addressed. Special attention should be paid to meteorological 

model performance in the desert southwest, as this was a recognized problem in the WRAP 

modeling. If possible, also consider including model performance plots similar to the bugle plots 

above on a site-by-site basis. 

2002 Planning Scenario

Input data used for the 2002 Planning model simulations consisted of the same meteorology as 

for the 2002 Base Case and the Plan02 emission inventories described under the Emissions 

Modeling section of the TSS. 

The setup of the CMAQ model (including science options, run scripts, simulation periods, and 

ancillary data) for the Plan02 cases was identical to that used in the Base02 modeling, as 

described in the 2002 MPE report (Tonnesen et al., 2006).

Comparison With Base02 Simulations
39

For each of the Plan02 emissions datasets, annual visibility modeling was performed using the 

CMAQ model. This was a key aspect of the QA procedure, since errors in the emissions 

inventories that might not be apparent during the emissions QA steps might be more readily 

detected in the results from the CMAQ modeling. 

Explanatory Comment: WRAP RMC compared CMAQ output using Plan02d emissions with 

CMAQ output using Base02b emissions by plotting the differences between the two air quality 

model runs for daily, monthly, and annual averages for each chemical species predicted. For a 

description and comparison of the Base02b and Plan02d emissions inventories, see section 3 

above. 

Note that these plots are not useful for visibility planning purposes, but are being provided to 

show the magnitudes of changes when moving from the 2002 Base Case to the 2002 Planning 

Case—in other words, from the actual emissions for the year 2002 to the “typical-year” 

emissions created for the final Plan02 scenario. The primary analysis “product” from the 

Plan02 CMAQ modeling is the use of its output in combination with the CMAQ output from the 

2018 modeling to develop the visibility progress calculations and glide path plots, described 

below.

2018 Model Simulations

The 2018 future-year base case scenario is referred to as “2018 Base Case” or “Base18”. The 

purpose of the Base18 scenario is to simulate the air quality representative of conditions in future 

year 2018 with respect to sources of criteria and particulate matter air pollutants, taking into 

consideration growth and controls. Modeling results based on this emission inventory are used to 

define the future year ambient air quality and visibility metrics.

Input data used for the 2018 Base Case model simulations consisted of the same meteorology as 

for the 2002 Base Case and the Base18 emission inventories described under the Emissions 

Modeling section of the TSS. 

The setup of the CMAQ model (including science options, run scripts, simulation periods, and 

ancillary data) for the Base18 cases was identical to that used in the Base02 modeling, as 

described in the 2002 MPE report (Tonnesen et al., 2006).

The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 

typical-year visibility modeling results, as discussed below. Some improvements in visibility by 

2018 are expected because of reductions in emissions due to currently planned regulations and 

technology improvements. 

Visibility Projections

The Regional Haze Rule (RHR) goals include achieving natural visibility conditions at 156 

federally mandated Class I areas by 2064. In more specific terms, that RHR goal is defined as (1) 

visibility improvement toward natural conditions for the 20% of days that have the worst 
40

visibility (termed “20% worst,” or W20%, visibility days) and (2) no worsening in visibility for 

the 20% of days that have the best visibility (“20% best,” or B20%, visibility days). One 

component of the states’ demonstration to EPA that they are making reasonable progress toward 

this 2064 goal is the comparison of modeled visibility projections for the first milestone year of 

2018 with what is termed a uniform rate of progress (URP) goal. As explained in detail below, 

the 2018 URP goal is obtained by constructing a “linear glide path” (in deciviews) that has at one 

end the observed visibility conditions during the mandated five-year (2000-2004) baseline period 

and at the other end natural visibility conditions in 2064; the visibility value that occurs on the 

glide path at year 2018 is the URP goal. 

Explanatory Comment: WRAP has made 2018 visibility projections using Plan02d and PRP18b 

CMAQ 36-km modeling results

[F]ollowing EPA guidance that recommends applying the modeling results in a relative sense to 

project future-year visibility conditions (U.S. EPA, 200134, 2003a35, 200636). Projections are 

made using relative response factors (RRFs), which are defined as the ratio of the future-year 

modeling results to the current-year modeling results. The calculated RRFs are applied to the 

baseline observed visibility conditions to project future-year observed visibility. These 

projections can then be used to assess the effectiveness of the simulated emission control 

strategies that were included in the future-year modeling. The major features of EPA’s 

recommended visibility projections are as follows (U.S. EPA, 2003a,b37, 2006):

• Monitoring data should be used to define current air quality.

• Monitored concentrations of PM10 are divided into six major components; the first five are 

assumed to be PM2.5 and the sixth is PM2.5-10.

o SO4 (sulfate)

o NO3 (particulate nitrate)

o OC (organic carbon)

o EC (elemental carbon)

o OF (other fine particulate or soil)

o CM (coarse matter).

• Models are used in a relative sense to develop RRFs between future and current predicted 

concentrations of each component.

• Component-specific RRFs are multiplied by current monitored values to estimate future 

component concentrations.

• Estimates of future component concentrations are consolidated to provide an estimate of 

future air quality.

 34 U.S. EPA. 2001. “Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze”, 

Draft Report, U.S. Environmental Protection Agency, Research Triangle Park, NC.

35 U.S. EPA. 2003a. “Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule.” EPA454/B-03-005. September 2003 36 U.S. EPA. 2006. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air 

Quality Goals for Ozone, PM2.5, and Regional Haze – Draft 3.2. U.S. Environmental Protection Agency, Office of 

Air Quality and Planning Standards, Research Triangle Park, North Carolina. September. 

(http://www.epa.gov/scram001/guidance/guide/draft_final-pm-O3-RH.pdf). 37 U.S. EPA. 2003b. “Guidance for Tracking Progress under the Regional Haze Rule.” U.S. EPA, EPA-454/B-03-

004. September 2003.
41

• Future estimated air quality is compared with the goal for regional haze to see whether the 

simulated control strategy would result in the goal being met.

• It is acceptable to assume that all measured sulfate is in the form of ammonium sulfate 

[(NH4)2SO4] and all particulate nitrate is in the form of ammonium nitrate [NH4NO3].

To facilitate tracking the progress toward visibility goals, two important visibility parameters are 

required for each Class I area:

• Baseline Conditions: “Baseline Conditions” represent visibility for the B20% and W20% 

days for the initial five-year baseline period of the regional haze program. Baseline 

Conditions are calculated using monitoring data collected during the 2000-2004 five-year 

period and are the starting point in 2004 for the uniform rate of progress (URP) glide path to 

Natural Conditions in 2064 (U.S. EPA, 2003a).

• Natural Conditions: “Natural Conditions,” the RHR goal for 2064 for the federally mandated 

Class I areas, represent estimates of natural visibility conditions for the B20% and W20% 

days at a given Class I area.

Mapping Model Results to IMPROVE Measurements

As noted above, future-year visibility at Class I areas is projected by using modeling results in a 

relative sense to scale current observed visibility for the B20% and W20% visibility days. This 

scaling is done using RRFs, the ratios of future-year modeling results to current-year results. 

Each of the six components of light extinction in the IMPROVE reconstructed mass extinction 

equation is scaled separately. Because the modeled species do not exactly match up with the 

IMPROVE measured PM species, assumptions must be made to map the modeled PM species to 

the IMPROVE measured species for the purpose of projecting visibility improvements. 

Projecting Visibility Changes Using Modeling Results

RRFs calculated from modeling results can be used to project future-year visibility. For the 

current modeling efforts, RRFs are the ratio of the 2018 modeling results to the 2002 modeling 

results, and are specific to each Class I area and each PM species. RRFs are applied to the 

Baseline Condition observed PM species levels to project future-year PM levels, which are then 

used with the IMPROVE extinction equation to assess visibility. The following six steps are used 

to project future-year visibility for the B20% and W20% visibility days (the discussion below is 

for W20% days but also applies to B20% days):

1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE data 

and IMPROVE extinction equation for each year from the five-year baseline period (2000-

2004) to identify the W20% visibility days for each year.

2. Use an air quality model to simulate a base-year period (ideally 2000-2004, but in reality just 

2002) and a future year (e.g., 2018), then apply the resulting information to develop Class-Iarea-specific RRFs for each of the six components of light extinction in the IMPROVE 

aerosol extinction equation.
42

3. Multiply the RRFs by the measured 24-h PM data for each day from the W20% days for each 

year from the five-year baseline period to obtain projected future-year (2018) 24-h PM 

concentrations for the W20% days.

4. Compute the future-year daily extinction using the IMPROVE aerosol extinction equation 

and the projected PM concentrations for each of the W20% days in the five-year baseline 

from Step 3.

5. For each of the W20% days within each year of the five-year baseline, convert the futureyear daily extinction to units of deciview and average the daily deciview values within each 

of the five years separately to obtain five years of average deciview visibility for the W20% 

days.

6. Average the five years of average deciview visibility to obtain the future-year visibility Haze 

Index estimate that is compared with the 2018 progress goal.

Review Comment: The six steps listed above are an appropriate application of national EPA 

modeling guidance for regional haze at the time the modeling was performed. 

Glide Path to Natural Conditions

A linear URP from the Baseline Conditions in 2004 to Natural Conditions in 2064 is assumed, 

and the value on the glide path at 2018 is the presumptive URP visibility target that the modeled 

2018 projections are compared against to judge progress.

Visibility Projection Results

For all of the WRAP Class I areas, the RMC performed preliminary 2018 visibility projections 

and compared them to the 2018 URP goals.

Explanatory Comment: WRAP performed visibility projections to 2018 and compared them to 

the 2018 URP goals using the Plan02d scenario for the base year period and the PRP18b 

scenario for 2018. These emissions scenarios were modeled with CMAQ and the resulting RRFs 

were applied to baseline monitoring data using the new IMPROVE equation. For a review of 

the 6 steps listed above to project future year visibility, see the review comments in Chapter 5.

Review Comment: A requirement for an approvable Regional Haze SIP is the establishment of 

reasonable progress goals that provide for an improvement in visibility for the most impaired 

days over the period of the implementation plan and ensure no degradation in visibility for the 

least impaired days over the same period. Region 6's preliminary review indicates that the 

visibility modeling performed by the WRAP results in modeled degradation of visibility 

conditions in 2018 at two Class I Areas in New Mexico on the 20% best days (Gila Wilderness 

and Carlsbad Caverns) and at Gila Wilderness on the 20% worst days. Initial examination into 

the cause of this modeled degradation reveal possible inconsistencies and/or errors between 

emission inventory scenarios that may be large enough to significantly affect model results at 

these Class I areas. A draft document containing the preliminary analysis can be obtained from 

Erik Snyder or Michael Feldman of EPA Region 6. This document details a preliminary review 

of some of the WRAP emission inventories and modeling results for New Mexico. Similar issues 

may affect model results at other Class I areas within the WRAP region. We note that the 
43

WRAP's modeling for 2018 also indicated that the following Class I areas (may not be a 

complete list) have modeled visibility degradation on the 20% worst days. 

MONT: Bob Marshall W, MT: Mission Mountains W, MT: Scapegoat W, MT

NOCA: Glacier Peak W, WA: North Cascades NP, WA

CHIR: Chiricahua NM NM, AZ: Chiricahua W W, AZ: Galiuro W, AZ 

6. Source Apportionment Modeling

The WRAP performed five types of attribution analyses to visibility-related data in an effort to 

better understand source region impacts of emissions on visibility. Discussed in the following 

subsections, those analyses include:

• PM Source Apportionment Technology (PSAT) analysis

• Weighted Emissions Potential analysis

• Organic Aerosol Tracer analysis

• Positive Matrix Factorization analysis

• Causes of Dust analysis

A. PM Source Apportionment Technology (PSAT) Analysis

Visibility impairment in Class I areas is the result of local air pollution as well as transport of 

regional pollution across long distances. The relative contributions to visibility impairment from 

each source region and category is needed to develop effective control strategies to improve 

visibility. WRAP used CAMx Version 4.30 with its Particulate Source Apportionment 

Technology (PSAT) tool to provide source apportionment by geographic regions and major 

source category. CAMx was run with similar options and inputs as the CMAQ modeling with 

both the 2002 baseline and 2018 future case emission inventories. PSAT uses reactive tracers 

that operate in parallel to the CAMx host model using the same emissions, transport, chemical 

transformation and deposition rates as the host model to account for the contributions of user 

specified source regions and categories to PM concentrations throughout the modeling domain. 

Details on the formulation of the CAMx PSAT source apportionment can be found in the CAMx 

user’s guidance38. The goals of the PSAT assessment are to evaluate the contributions of 

different geographic regions and source categories to visibility impairment at Class I areas in 

2002 and the projected 2018 case in order to identify those regions and source categories that, if 

controlled, would produce the greatest improvements in visibility. Further information regarding 

the PSAT analysis technique can be found in the TSS document PM Source Apportionment 

Technology (PSAT) Analysis39

The WRAP defined 18 geographical source regions consisting of the individual WRAP states, 

Nevada, the CENRAP states, Canada, Mexico, Pacific off-shore and the remainder of the eastern 

U.S. including the Gulf of Mexico and the Atlantic (Table 6.1, Figure 6.1). Six source categories 

 38 ENVIRON. 2006. “User’s Guide – Comprehensive Air-quality Model with extensions, Version 4.30.” ENVIRON 

International Corporation, Novato, California. (available at http://www.camx.com). 

39 http://vista.cira.colostate.edu/docs/wrap/attribution/PSATMethods.doc
44

(point, mobile, area, anthropogenic wildfires (WRAP), natural or non-anthropogenic sources 

(WRAP), and sources outside of the modeling domain) were tracked separately. The PSAT 

modeling focuses on sulfate and nitrate contribution only and takes into account chemistry and

deposition. Contributions for the 20% worst and 20% best days at each WRAP and nearby Class 

I area were extracted from the PSAT results. A PSAT Visualization Tool was developed that 

can be used by States, Tribes and others to generate displays of the contributions of source 

regions and categories to visibility impairment for the average of the worst 20 percent and best 

20 percent days at each WRAP and nearby Class I areas40.

PSAT results rely on the model’s value for each of the individual modeled species in calculating 

the contribution. Therefore, the PSAT results are directly impacted by model performance 

issues. In reviewing PSAT results, an evaluation of model performance for that species for the 

specific Class I monitor that is being evaluated should be taken into consideration. EPA does 

not have any specific guidance on how to conduct source apportionment modeling. The WRAP 

used state-of-the-science source apportionment tools within a widely used photochemical model. 

EPA has reviewed the PSAT tools and techniques that were used in the PSAT analysis and 

considers the analysis acceptable. 

For sites where the state establishes a reasonable progress goal that does not meet the Uniform 

Rate of Progress to attain natural conditions by 2064, the state must demonstrate that the rate of 

progress for the implementation plan to attain natural conditions by 2064 is not reasonable and 

that the progress goal adopted by the State is reasonable. PSAT results along with other source 

apportionment techniques and analysis of emission inventories is required to determine the 

causes of future visibility impairment and support the conclusion that the proposed RPG is 

reasonable.

Table 6.1. WRAP CAMx/PSAT source regions.

Source 

Region ID

Source Region 

Description

Source 

Region ID

Source Region 

Description

1 Arizona (AZ) 10 South Dakota (SD)

2 California (CA) 11 Utah (UT)

3 Colorado (CO) 12 Washington (WA)

4 Idaho (ID) 13 Wyoming (WY)

5 Montana (MT) 14 Pacific off-shore & Sea of Cortez 

(OF)

6 Nevada (NV) 15 CENRAP states (CE)

7 New Mexico (NM) 16 Eastern U.S., Gulf of Mexico, & 

Atlantic Ocean (EA)

8 North Dakota (ND) 17 Mexico (MX)

9 Oregon (OR) 18 Canada (CN)

 40 available at http://vista.cira.colostate.edu/TSS/Results/SA.aspx
45

-2736 -2412 -2088 -1764 -1440 -1116 -792 -468 -144 180 504 828 1152 1476 1800 2124 2448

-2088

-1872

-1656

-1440

-1224

-1008

-792

-576

-360

-144

72

288

504

720

936

1152

1368

1584

1800

2 6

9

12

4

5 8

13

11

3

1 7

15

15

16

16

16

18 18

14

17

14

10

Figure 6.1. WRAP CAMx/PSAT source region map. Table 6 defines the source region IDs.

Review Comment: The CAMx model selection and performance are reviewed elsewhere in this 

document. 

Review Comment: The CAMx PSAT analysis has been tested and evaluated against other 

apportionment techniques41,42. 

Review Comment: PSAT results from CAMx utilized the Plan02c and Base18b emission 

inventories, while CMAQ modeling to derive RRFs and the WEP analyses are performed with 

the Plan02d and PRP18b emission inventories. There are a number of differences between these 

emission inventories (e.g. Plan02c does not include any biogenic emissions of NOx or VOC from 

Mexico; PRP18b includes projected emissions for Mexico) that impact visibility projections and 

must be considered when interpreting results. 

B. Weighted Emissions Potential

The Weighted Emissions Potential (WEP) analysis was developed as a screening tool for states 

to decide which source regions have the potential to contribute to haze formation at specific 

Class I areas, based on both the Baseline and 2018 emissions inventories. Unlike the PSAT 

analysis described above, this method does not account for chemistry and removal processes. 

 41 Morris, R.E., G.Y., C.E., G.W., B.K. 2005. “Recent Advances in One-Atmospheric Modeling Using the 

Comprehensive Air-quality Model with Extensions.” Presented at the 98th Annual Air and Waste Management 

Conference, Minneapolis, MN. June.

42 Yarwood, G., R.E. Morris, G. Wilson. 2004. “Particulate Matter Source Apportionment Technology (PSAT) in 

the CAMx Photochemical Grid Model." Presented at the ITM 27th NATO Conference- Banff Centre, Canada, 

October. 
46

Instead, the WEP analysis relies on an integration of gridded emissions data, meteorological 

back trajectory residence time data, a one-over-distance factor to approximate deposition, and a 

normalization of the final results. Residence time over an area is indicative of general flow 

patterns, but does not necessarily imply the area contributed significantly to haze at a given 

receptor. Therefore, users are cautioned to view the WEP analysis as one piece of a larger, more 

comprehensive weight of evidence analysis. Further information regarding the WEP analysis 

technique can be found in the TSS document Weighted Emissions Potential Analysis43.

The emissions data used were the annual, 36km grid SMOKE-processed, model-ready emissions 

inventories provided by the WRAP Regional Modeling Center (RMC)44. The analysis was 

performed for nine (9) pollutants (maps were generated for all but the last three): sulfur oxides, 

nitrogen oxides, organic carbon, elemental carbon, fine particulate matter, coarse particulate 

matter, ammonia, volatile organic carbon, carbon monoxide. The following source categories for 

each pollutant were identified and preserved through the analysis: Biogenic, Natural fire, Point, 

Area, WRAP oil and gas, Off-shore, On-road mobile, Off-road mobile, Road dust, Fugitive dust, 

Windblown dust, Anthropogenic fires 

The back trajectory residence times were provided by the WRAP Causes of Haze Assessment 

(COHA)45. The COHA project used NOAA’s HYSPLIT model to generate eight (8) back 

trajectories daily for each WRAP Class I area for the entire five-year baseline period (2000-04). 

The major model parameters selected for this analysis are presented in Table 6.2. From these 

individual trajectories, residence time fields were generated for one-degree latitude by onedegree longitude grid cells. Residence time analysis computes the amount of time (e.g., number 

of hours) or percent of time an air parcel is in a horizontal grid cell. Plotted on a map, residence 

time is shown as percent of total hours in each grid cell across the domain, thus allowing an 

interpretation of general air flow patterns for a given Class I area. The residence time fields for 

the 20% worst and best IMPROVE-monitored extinction days were selected for the WEP 

analysis to highlight the potential emissions sources during those specific periods.

Table 6.2. Back Trajectory Model Parameters Selected for WEP Analysis

Model Parameter Value

Trajectory duration 192 hours (8 days) backward in time

Top of model domain 14,000 meters

Vertical motion option used model data

Receptor height 500 meters

Meteorological Field EDAS and FNL (location dependent)

Review comment: The receptor height of 500 meters may be more appropriate for secondary 

particulate matter than for course particulate matter. 

The WEP analysis consisted of weighting the annual gridded emissions (by pollutant and source 

category) by the worst and best extinction days residence times for the five-year baseline period. 

To account for deposition along the trajectories, the result was further weighted by a one-over-

 43 http://vista.cira.colostate.edu/docs/wrap/attribution/WEPMethods.doc 44 http://pah.cert.ucr.edu/aqm/308/ 45 http://coha.dri.edu/index.html
47

distance factor, measured as the distance in km between the centroid of each emissions grid cell 

and the centroid of the grid cell containing the Class I area monitoring site under investigation. 

(The “home” grid cell of the monitoring site was weighted by one fourth of the 36km grid cell 

distance, or one-over-9km, to avoid a large response in that grid cell.) The resulting weighted 

emissions field was normalized by the highest grid cell to ease interpretation. Interpretation of 

the results should focus on which grid cells (or larger regions) have significant potential to affect 

the Class I area, and on changes between 2002 and 2018.

The WEP is not a rigorous, stand-alone analysis, but a simple, straightforward use of existing 

data. As such, there are several caveats to keep in mind when using WEP results as part of a 

comprehensive weight of evidence analysis: 

• This analysis does not take into account any emissions chemistry.

• While actual emissions may vary considerably throughout the year, this analysis pairs up 

annual emissions data with 20% worst/best extinction days residence times – this is likely 

most problematic for carbon and dust emissions, which can be highly episodic.

• Coarse particle and some fine particle dust emissions tend not to be transported long 

distances due to their large mass.

• The WEP results are unitless numbers, normalized to the largest-valued grid cell. Effective 

use of these results requires an understanding of actual emissions values and their relative 

contribution to haze at a given Class I area.

Review comment: The Weighted Emissions Potential (WEP) analysis has a number of 

limitations, listed above. However, it may be useful as a screening tool for states to decide 

which source regions have the potential to contribute to haze formation at specific Class I areas.

C. Organic Aerosol Tracer

Contributors to organic carbon aerosols (OC) were evaluated as part of the weight of evidence 

for the clean conditions scenario. The CMAQ model results were analyzed to identify primary 

organic carbon aerosol source contributions as originating in one of three categories:

• Primary anthropogenic OC resulting from direct organic mass emissions, such as primary 

organic aerosol (POA). In CMAQ these species are lumped into the term AORGPA.

• Secondary anthropogenic OC resulting from aromatic VOCs, such as xylene, toluene, and 

cresols. In CMAQ these species are lumped into the term AORGA.

• Secondary biogenic OC resulting from biogenic VOCs, such as terpenes. In CMAQ these 

species are lumped into the term AORGB.

This analysis did not include identification of emissions source regions or detailed source 

category information. Because it was not cost effective to carry out CAMx/PSAT simulations 

with OC, the explicit OC results for the clean conditions case were analyzed, and then compared 

to the Base02b case in an attempt to infer the relative contributions of biogenic and 

anthropogenic VOCs to OC. These results are difficult to interpret for at least two reasons: 
48

• Because of the simplified approach used by CMAQ and the Carbon Bond Mechanism 

version 4 (CB4) to represent these species, it is not possible to accurately classify all 

emissions into the CMAQ model as either biogenic or anthropogenic based simply on the 

species name. Thus, some biogenic OC might be included with AORGA, and some 

anthropogenic OC might be included in AORGB. 

• Some fire emissions are classified as anthropogenic, but these emissions might include 

species such as terpenes that are typically considered biogenic. Hence, using this simplified 

approach can be misleading. 

In spite of these difficulties, however, the results should classify the majority of the emissions 

correctly as either biogenic or anthropogenic. For each of the above three components of OC, 

plots of the annual average mass in the Base02b case were prepared, and then estimated the 

controllable mass as the difference between the Base02b case the Base02nt clean emissions 

scenario. Comparing these two scenarios indicates that in the western U.S. there is considerable 

AORGPA mass that is not controllable. It is likely that much of this mass is from fires, since 

uncontrollable AORGPA mass is present at the site of large fires in southern Oregon and north of 

Tucson, AZ. It might be difficult for the WRAP states and tribes to use these results 

quantitatively in developing emissions control strategies for visibility SIPs and TIPs. However, 

the results do provide some insight into the relative contributions of biogenic and anthropogenic 

OC as well as the amount of each that is controllable in the model simulations.46 

There are uncertainties in the modeled emissions of anthropogenic VOCs, and larger 

uncertainties in the modeled emissions of biogenic VOCs. It is not possible to evaluate the model 

performance individually for biogenic and anthropogenic OC because the OC measurements do 

not distinguish between those two forms. Instead, we can only compare total modeled OC to 

total measured OC. Therefore, even when the model achieves good performance for total OC, it 

is possible that the model may be overpredicting one component of total OC and underpredicting 

the other. The inability to evaluate model performance for each component of OC increases the 

uncertainty of the results, so caution should be used when drawing conclusions about the sources 

of OC based on these results. 

Review comment: WRAP model achieves good performance for total OC, as discussed in 

Section 5, figure 5.1 of this document. However, as stated above, because of the potential for 

compensatory errors, caution should be used when drawing conclusions regarding the source of 

OC based on these results. 

D. Positive Matrix Factorization

As part of their Causes of Haze Analysis (CoHA) project for WRAP, Desert Research Institute 

(DRI) performed a Positive Matrix Factorization (PMF) analysis using the IMPROVE aerosol 

data set and meteorological back trajectories. PMF is one among several receptor modeling 

methods. Receptor modeling is a fundamentally different approach to determining source 

contributions to visibility impairment in that they are based on a statistical analysis of the 

monitoring data collected at the IMPROVE monitoring sites, and not on dispersion modeling. 

 46 Further information regarding the Organic Aerosol Tracer analysis technique can be found in the TSS document 

http://pah.cert.ucr.edu/aqm/308/reports/final/2006/WRAP-RMC_2006_report_FINAL.pdf, page 73.
49

The purpose of this analysis was to distinguish chemical source profiles which could describe 

aerosol contributions to IMPROVE monitoring sites within the WRAP region. Through a 

review of source profile characteristics, profiles were identified with general or specific 

emissions source categories, such as “Smoke” or “Urban/Diesel” or any of several other 

categories. Percent contributions of each profile to each IMPROVE site’s aerosol concentrations 

were derived and compared with emissions inventories to evaluate the level of confidence in the 

results.47

In order to identify the sources of aerosols in the western United States, Positive Matrix 

Factorization (PMF) receptor model was applied to the 24-hr integrated aerosol chemical 

composition data obtained at the Class I areas of the Western Regional Air Partnership (WRAP)

region through the Interagency Monitoring of Protected Visual Environments (IMPROVE) 

program. The IMPROVE sites in the WRAP region were grouped into 18 sub-regions (including 

West Texas region). PMF is applied to each group to generate profiles of source factors. 

Normalized source profiles and the quantitative source contributions for each resolved factor 

were calculated. The major sources that contribute to the aerosol loadings in the western United 

States were identified. The similarities and differences of chemical source profiles, and the major 

aerosol source contributors in different regions of the western United States were investigated. 

Based on the profile and the daily contribution to aerosol concentration of each source factor, the 

contributions of source factors to the aerosol light extinction coefficients were estimated using 

the IMPROVE method. The importance of the major aerosol sources to regional haze and 

visibility in the Class I areas of the western United States is discussed. 

A trajectory analysis was used to show the major source regions of sources (factors) defined by 

PMF. These results were compared with the emissions inventories data to evaluate level of 

confidence in results.

Review comment: The PMF and trajectory analysis may be fairly robust for the source factors 

identified. However, EPA believes that the grouping of IMPROVE sites into 18 sub-regions, 

while less resource intensive, gives results that are less useful than conducting PMF modeling 

for individual IMPROVE sites. If PMF modeling will be used in future regional haze technical 

analyses, then each individual IMPROVE site should be modeled. In future applications, PMF 

contribution results should be compared to other modeling methods such as CAMx using PSAT. 

Such a comparison could be of significant utility in evaluating upwind emission inventories used 

as inputs to models such as CAMx and CMAQ. 

E. Causes of Dust Analysis

As part of their CoHA project for WRAP, DRI performed a Causes of Dust Analysis, designed to 

characterize aerosol sampling days when coarse mass and fine soil combined constituted the 

dominant aerosol extinction species. In addition to categorizing dust events, the project was also 

able to identify a number of temporal trends.48

 47 Further information regarding the PMF analysis can be found on the CoHA web site 

http://www.coha.dri.edu/web/general/tools_PMFModeling.html. 48 Further information regarding the Causes of Dust analysis can be found on the CoHA web site 

http://www.coha.dri.edu/dust/index.html.
50

The principal aim of the study was to specifically identify the primary causes of dust measured in 

the WRAP region by: 

• developing a methodology for assigning worst days when dust constituted the largest 

contributor to aerosol visibility extinction (worst dust days, hereafter) at IMPROVE monitors 

within the WRAP domain to a set of source classes; and

• using the methodology to categorize worst dust days over the period 2001 – 2003. 

The methodology employs several existing tools in novel ways including air mass backward 

trajectories, land use maps, and soil characteristics maps. In addition, two new methods have 

been developed as part of this work. The first is a metric for estimating the contribution of Asian 

dust to IMPROVE-measured dust on worst dust days. The second utilizes multivariate linear 

regression of measured dust concentrations vs. nominally local surface meteorological data. 

These tools were combined using a semi-quantitative approach to preliminarily determine the 

likely source of dust on a worst dust day at a given site. Due to limitation of the information and 

capabilities of the tools, the causes of some worst dust days were not determined with any 

confidence. Using 2001-2003 data from IMPROVE (and some protocol) monitors in the WRAP 

regions, each worst dust day was associated with one of these events:

• Transcontinental transport of large scale events from Asia

• Windblown dust events

• Transport of windblown dust from sources upwind (i.e. not from immediate vicinity of site). 

• Further specification if windblown and upwind transport events appear to be regional in 

nature based on scale of meteorological phenomenon causing dust and number of sites 

affected.

• Undetermined Events

This study focused on 71 sites from the IMPROVE network (and protocol sites) located in the 

WRAP domain. These sites were selected based on availability of data over the 2001 – 2003 

period and the availability of a nearby surface meteorological station over the same period. 

The transport of airborne dust emitted from high wind events originating in China to the west 

coast of the US (about 7 – 10 days en route) has received considerable attention in recent years 

(Cheng et al., 200549; Park et al., 200550; Zhang et al., 200551; Darmenova et al., 200552

 49 Cheng, TT, Lu, DR, Wang, GC, Xu, YF. Chemical characteristics of Asian dust aerosol from Hunshan Dake 

sandland in Northern China. Atmospheric Environment, 2005. 2903-2911

). Large 

“Asian dust” events can contribute significantly to haze over large portions of the western US. 

These large Asian dust episodes are initiated by low pressure systems in the Gobi desert region 

of Mongolia and northwest China. Once elevated to the troposphere, Asian dust can move fast 

50 Park, SU, Chang, LS, Lee, EH. Direct radiative forcing due to aerosols in East Asia during a Hwangsa(Asian dust) 

event observed on 19-23 March 2002 in Korea. Atmospheric Environment, 2005, 2593-2606 51 Zhang, RJ, Arimoto, R, An, JL, Yabuki, S, Sun, JH. Ground observations of a strong dust storm in Beijing in 

March 2002. Journal of Geophysical Research, 2005, D18S06

52 Darmenova, K, Sokolik, IN, Darmenov, A. Characterization of east Asian dust outbreaks in the spring of 2001 

using ground-based and satellite data. Journal of Geophysical Research, 2005 D02204
51

under zonal flow due to the jet stream. Under high pressure ridge conditions, large-scale 

exchange of dust from the troposphere to the boundary layer may occur resulting in elevated 

ground-level mineral aerosol concentrations. Although it is difficult to quantitatively separate the 

influence of the Asian dust from dust that is generated on the North American continent or 

transported from other regions of the world (e.g. Africa), some chemical markers can help 

identify dust of Asian origin. Perry et al. [1997]53 and Van Curen and Cahill [2002]54 suggested 

that Al/Ca and K/Fe ratios are useful for identifying Asian and African dust. African dust is 

associated with Al/Ca ratios greater than 3.8, while those ratios for Asian dust are generally less 

than 2.6. The K/Fe ratio is consistently above 0.5 for Asian dust, while African dust exhibits 

lower values for this ratio. Similar chemical markers have been adopted to help distinguish Asian 

dust from dust generated on the North American continent for this study. The large Asian dust 

storm on April 19, 1998 was used as a benchmark for establishing these markers. The dust plume 

from the 4/19/1998 storm crossed the Pacific Ocean, and subsided to the surface of the western 

United States around 4/29/1998.

A total of 644 worst dust days, defined as 20% worst visibility days when the sum of extinction 

from coarse mass (CM) and (FS) was larger than any other component, were observed during the 

period 2001-2003. Using the tools described in the report, it was found that:

i. approximately 50% (318 cases) of worst dust days were attributable, with a moderate 

(***) to high (*****) degree of confidence, the following events/classes:

o Transcontinental transport from Asia: 48 cases (7.5%);

o Windblown dust (generated locally in the vicinity – nominally within 10 km - of 

the site): 125 cases (19.4%);

o Upwind transport (does not involve significant windblown dust from sources local 

to the site): 145 cases (22.5%);

ii. Approximately 30% (190 cases) of the remaining days were attributed to the following 

events/classes with a low (*) to moderate (***) degree of confidence:

o Transcontinental transport from Asia: 7 cases (1.1%);

o Windblown dust: 76 cases (11.8%);

o Upwind transport: 107 cases (16.6%);

iii. The remaining 21% of worst dust days (136 case) were not attributable to any 

events/classes using the tools employed in this study.

A number of temporal trends were also observed, both in terms of frequency of event occurrence 

and in terms of worst dust days resulting from undetermined sources. The impact of 

transcontinental transport from Asia was only observed during spring (100% of 48 cases). 

Windblown dust as a dust causing event was most important in spring (56.8% of 125 cases), 

while transport from upwind sources did not vary significantly by season except for a notable 

decrease in the winter months (spring: 35% of 145 cases, summer: 28%, fall: 31%, winter: 6%).

 53 Perry KD, Cahill TA, Eldred RA, Dutcher DD, Gill T, 1997, Long-range transport of North African dust to the 

eastern United States Journal of Geophysical Research 102 (D10): 11225-11238 54 VanCuren RA, Cahill TA Title: Asian aerosols in North America: Frequency and concentration of fine dust. 

Journal of Geophysical Research 107 (D24): Art. No. 4804 DEC 28 2002
52

For the sites considered in this study, worst dust days exhibited a seasonal pattern, with the most 

frequent occurrences in summer (246 out of 644) and spring (241). The fall (115) and winter (43) 

were associated with significantly fewer worst dust days. Of the 644 total worst dust days, a total 

of 136 were a result of events/sources that could not be determined using the tools employed in 

this study. The greatest number of undetermined events occurred in the summer, corresponding 

to 79 cases (32% of summer worst dust days), followed by spring (23 cases, 10% of all spring 

worst dust days) and fall (22 cases, 19% of all fall worst dust days), and winter (12 cases, 28% of 

all winter worst dust days).

The results of four example case studies were examined and are presented below:

April 16, 2001 

On April 16, 2001, 29 sites were classified as worst dust days. For 22 sites, the Asian Dust Score 

indicated a strong Asian signature. Satellite and Naval Research Laboratory model results 

corroborated a large Asian dust plume engulfing a large portion of the West coast. 

September 10, 2001 

On September 10, 2001, 5 sites located in Arizona were classified as worst dust days. For all 

sites, the ADS was low (or not calculated), suggesting a negligible contribution of transported 

Asian dust. Trajectory analysis for all sites indicated moderate-to high speed trajectories over 

areas with moderate-high erodibility in southeast Arizona, south New Mexico and east/southeast 

Texas.

July 06, 2001 

On July 06, 2001, 4 sites were classified as worst dust days. At Colombia River Gorge (CORI) 

the LWD to Total measured dust ratio was 40.5%. However, back trajectories did not show 

sustained high winds over moderately (or highly) erodible terrain. Thus, CORI was assigned to a 

windblown event. At Bandelier (BAND) the LWD to total measured dust ratio was ~ 6% and 

trajectories showed some high winds over moderately erodible terrain. BAND was associated 

with windblown event. The information available for Nearby San Pedro (SAPE) And Gila 

(GICL) did not provide any indication of the event that may have caused a worst dust day there.).

April 03, 2003 

Great Sand Dunes (GRSA), Weminuche Wilderness (WEMI), and Rocky Mountain (ROMO) 

had worst dust days on April 03, 2003. For all three sites, the event was flagged as a regional 

scale event since the same general flow pattern caused all three sites to have worst dust days.

Review comment: The Assessment of the Principal Causes of Dust-Resultant Haze at IMPROVE 

Sites in the Western United States provides a reasonable basis for the attribution of 

transcontinental transport form Asia. The techniques employed were relatively more successful 

for spring events (10% of events could not be determined) than fall (19%), winter (28%) and 

summer events (32%).

7. BART modeling 
53

The Clean Air Act establishes the national goal of eliminating man-made visibility impairment 

from all Class I areas. As a part of the plan for achieving this goal Section 169A(b)(2)(A) of the 

act requires certain major stationary sources in existence between 1962 and 1977 to be reviewed 

for BART. Pursuant to federal regulations, states have the option of exempting a BART-eligible 

source from the BART requirements based on dispersion modeling demonstrating that the source 

cannot reasonably be anticipated to cause or contribute to visibility impairment in a Class I area. 

According to 40 CFR Part 51, Appendix Y, a BART eligible source is considered to “contribute” 

to visibility impairment in a Class I area if the modeled 98th percentile change in deciviews is 

equal to or greater than the “contribution threshold.” Any BART-eligible source determined to 

cause or contribute to visibility impairment in any Class I area is subject to BART. The EPA 

BART Guidelines recommend a contribution threshold of 0.5 change in deciview be used, 

although States have the option to establish alternative thresholds.

To determine whether a source exceeds the BART contribution threshold, EPA recommends use 

of the CALMET/CALPUFF modeling system; the main components of this modeling system are 

CALMET (a diagnostic three-dimensional meteorological model), CALPUFF (an air quality 

dispersion model), and CALPOST (a post-processing package). Seven WRAP region States 

(Arizona, Montana, New Mexico, Nevada, South Dakota, Utah, and at the request of EPA 

Region 8, Montana) requested that the WRAP regional modeling center perform BART 

exemption screening modeling to help determine whether potential BART-eligible sources in 

their states contribute significantly to visibility impairment at a Class I area. In response WRAP 

conducted a modeling analysis and provided spreadsheets of Calpuff modeled 24-hour average 

change in deciview from each potential BART-eligible source and each Class I area. That 

CALMET/CALPUFF modeling effort is described on the RMC55 and WRAP56 websites. Note 

that in several of these States the WRAP modeling discussed below provided a foundation for 

more refined or supplemental BART–exemption modeling conducted by the individual States. A 

description and evaluation of these additional modeling analyses is contained in the individual 

Regional Haze SIPs submitted by each State. 

A. Overview of WRAP Modeling Approach

To conduct the modeling, WRAP followed the EPA BART guidelines (U.S. EPA, 2005) and the 

applicable CALMET/CALPUFF modeling guidance (e.g., IWAQM, 1998; FLAG, 2000; EPA, 

2003c) in effect at the time the analysis was conducted. This included EPA’s March 16, 2006, 

memorandum “Dispersion Coefficients for Regulatory Air Quality Modeling in CALPUFF” 

(Atkinson and Fox, 2006). This memo was written by the EPA Office of Air Quality and 

Planning Standards (OAQPS) Model Clearinghouse Director in response to questions from EPA 

Region 4 on what constitutes the regulatory version of CALPUFF and recommended CALPUFF 

options for BART modeling in their region. The memo’s modeling recommendations are 

followed in WRAP’s protocol for conducting the modeling57

 55

, except as noted below. Using the 

procedures in the modeling protocol, WRAP conducted initial subject-to-BART screening 

analysis modeling to determine which sources contribute significantly to visibility impairment at 

Class I areas. WRAP then provided the modeling results to the affected states, and the states used 

http://pah.cert.ucr.edu/aqm/308/bart.shtml 56 http://pah.cert.ucr.edu/aqm/308/reports/final/2006/WRAP-RMC_2006_report_FINAL.pdf 57 http://pah.cert.ucr.edu/aqm/308/bart/WRAP_RMC_BART_Protocol_Aug15_2006.pdf
54

those results, in conjunction other information, to determine whether or not the individual 

BART-eligible source will be subject to BART control requirements. For some parameters 

WRAP modeled multiple scenarios and deferred to the States to select which modeled parameter 

is most appropriate. For example WRAP modeled background visibility conditions 

corresponding to both the 20% best visibility days and annual average visibility days. EPA 

guidance allows States to select either characterization of background conditions for their BART 

modeling. WRAP also provided modeling results showing the highest 24 average change in 

visibility and the 8th highest (98th percentile) predicted change. States may utilize the maximum 

result if a conservative screening analysis is deemed appropriate. The Regional Haze SIPs 

submitted by each State should be reviewed to determine which of the WRAP BART modeling 

options was selected for use by the State. 

B. Model Selection and Applicability

Relevant guidance (IWAQM, 199858; FLAG 200059; EPA, 200360) states that the CALPUFF 

model is generally applicable at distances from 50 km to at least 200-300 km downwind of a 

source. Given the large number of Class I areas in the West, most BART-eligible stationary 

sources modeled by WRAP were located within 300 km of at least one Class I area. In some 

cases where the transport distance to the nearest Class I area exceeded 300 km the States, in 

consultation with EPA and the FLM’s, have conducted supplemental analysis for their regional 

haze BART submittals.

The WRAP modeling was conducted using CALPUFF version 6.0. At the time of the analysis 

the Calpuff regulatory version was v 5.711, however discussions with the Federal Land 

Managers (FLMs) and others revealed that this version of the model contained software bugs and 

that a newer version should be used. EPA’s regulatory version has subsequently been revised to 

version 5.8 which should be used in new regulatory applications. Calpuff version 6.0 (and 

version 5.711) is considered obsolete; however, it was considered appropriate at the time of the 

analysis and is therefore acceptable for use in State regional haze plans that have relied on 

WRAPs modeling. 

C. Modeling Domain and Meteorology Inputs

The WRAP RMC developed a set of CALMET/CALPUFF modeling domains in the contiguous 

U.S. that focus on the following states and nearby Class I areas: Arizona, Montana, New Mexico, 

Nevada, South Dakota, and Utah. In addition, there is an Alaska CALMET/CALPUFF modeling 

domain. The dimensions of the domain were selected to include the State of interest, plus Class I 

areas in nearby states, and to provide a sufficient buffer between the Class I areas and the domain 

boundaries (e.g., 50 km) to assure that CALPUFF puffs are not eliminated that may temporarily 

leave the domain and later reemerge and cause visibility impacts at a Class I area. WRAP 

 58 Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for 

Modeling Long Range Transport Impacts (EPA-454/R-98-019)

59 http://www.nature.nps.gov/air/Pubs/pdf/flag/FlagFinal.pdf 60 EPA. 2003. “Revisions to the Guideline on Air Quality Models: Adoption of a Preferred Long Range Transport 

Model and Other Resources”; Final Rule. Fed. Reg./Vol. 68, No. 72/Tuesday April 15, 2003/Rules and Regulations. 

40 CFR51.
55

applied CALPUFF/CALMET for each of these domains. For the five continental-U.S. domains, 

WRAP used a 4-km grid and 11 vertical layers with a Lambert conformal conic (LCC) map 

projection identical to that used for the WRAP MM5 and CMAQ modeling (see Section 1.3 of 

the WRAP 2002 MPE report

61. The CALMET modeling for these five states used MM5 

meteorological data at 36-km resolution and terrain and land use data at 4-km resolution, along 

with surface meteorological and precipitation observations. For the WRAP Alaska 

CALMET/CALPUFF modeling, a 2-km grid was used, along with 15-km-resolution MM5 data, 

surface meteorological and precipitation data, and 1-km-resolution land use and terrain data. For 

CALMET modeling for the continental-U.S. states, WRAP used MM5 meteorology data for the 

years 2001, 2002 and 2003. EPA recommends performing three years of CALMET/CALPUFF 

modeling when using MM5 data, and 2001 through 2003 were the most recent three years with 

MM5 data available at the time this modeling was initiated. For Alaska CALMET modeling, 

WRAP used one year of data (2002) because only one year of historical MM5 modeling is 

available. 

Note that although surface meteorological and precipitation observations were used as input to 

CALMET, upper-air meteorological observations were not. EPA and the FLMs generally 

recommend both surface and upper air information be blended into the CALMET modeling, 

although in some applications they may be redundant to the MM5 data which already includes 

the upper air data. 

D. Emissions Input

According to the EPA BART Guidelines: “The emissions estimates used in the models are 

intended to reflect steady-state operating conditions during periods of high capacity utilization. 

We do not generally recommend that emissions reflecting periods of start-up, shutdown, and 

malfunction be used, as such emission rates could produce higher than normal effects than 

would be typical of most facilities. We recommend that States use the 24 hour average actual 

emission rate from the highest emitting day of the meteorological period modeled, unless this 

rate reflects periods start-up, shutdown, or malfunction.” (EPA, 2005)62.

As a first approach to identify BART eligible sources and the appropriate modeling emission 

rates, WRAP used SCC-based BART eligibility criteria and information provided by ERG, Inc., 

in their report “Identification of BART-eligible Sources in the WRAP Region” (ERG, 2006)

63

 61

. 

WRAP then provided spreadsheets with this information to the states and tribes for comment. 

The States/Tribes reviewed this information along with NSR permitting and other records and 

determined which sources are BART eligible. Sources from the 7 states determined to be BART 

eligible were then modeled using emission estimates provided by the state air agency and in the 

case of Montana BART-eligible sources, by EPA Region 8 based on the maximum 24 hour 

emission rates during the 2001-2003 meteorological period modeled. In-stack continuous 

http://pah.cert.ucr.edu/aqm/308/docs.shtml 62 EPA. 2005. “Regional Haze Regulations and Guidelines for Best Available Technology (BART) Determinations”. 

Fed. Reg./Vol. 70, No. 128/Wed. July 6, 2005, Rules and Regulations, pp. 39104-39172. 40 CFR Part 51, FRL7925-9, RIN AJ31. 63 ERG, Inc., 2006: Identification of BART-eligible Sources in the WRAP Region, Draft Report. Prepared for the 

Western Regional Air Partnership by ERG, Inc., Sacramento, CA. April 4, 2006
56

emission monitoring (CEM) data were used where available. For sources where CEM data were 

not available permitted allowable or calculated AP-42 emission rates were used. For source 

specific emissions information the individual State regional haze SIP should be consulted. 

E. Calpuff/Calmet Model Settings

The CALMET and CALPUFF model settings used by WRAP complied with EPA’s 

recommendations for regulatory application of the CALPUFF modeling system in effect at the 

time of the analysis, except as noted below:

i. The maximum allowable mixing height (Z1MAX) for the WRAP CALMET modeling 

was 4,500 m AGL, versus the EPA regulatory default of 3,000 m AGL. Vertical 

temperature soundings indicate higher summertime mixing heights are appropriate in 

many areas of the intermountain west. 

ii. The EPA default assumes no MM5 data will be used (IPROG=0). In the RMC BART 

screening analysis, MM5 data were used as an initial-guess field (IPROG=14). 

iii. The EPA default assumes values for IEXTRP (-4) and RMIN2 (4) that are incompatible 

with each other in some of the applications and therefore used values of 1 and 4, 

respectively. WRAP used the hourly 36-km MM5 data to define the upper-level winds, 

thus the extrapolation of the surface wind data aloft as recommended by EPA (IEXTRP= 

-4) was not needed. 

iv. WRAP set IAVET to 0 to turn off spatial averaging of the temperature interpolation, 

since the MM5 temperatures are already fairly smooth. 

v. The beginning and ending water land use categories were changed to 51 and 55, 

respectively, rather than using the EPA default (999) that assumes no water land use 

categories. 

F. Model Evaluation

The CALMET meteorological fields were spot-checked for reasonableness using visualization 

and animation tools, but no comprehensive evaluation was undertaken. The main inputs to 

CALMET are the hourly 36-km MM5 data (15-km MM5 data for Alaska), surface 

meteorological and precipitation measurements, terrain data, and land use data. The 36-km MM5 

fields were evaluated previously (McNally, 200364; Kemball-Cook et al., 200565; Baker, 

2004a66,b67

 64 McNally, D.E. 2003. Annual Application of MM5 for Calendar Year 2001. Prepared for U.S. EPA, Office of Air 

Quality and Planning Standards. Prepared by Alpine Geophysics, Arvada, CO.

), and the terrain and land use inputs were evaluated by comparing spatial plots of the 

inputs against topographic and land use maps. The Alaska 15-km MM5 data were also evaluated 

(Kemball-Cook et al., 2005). EPA has reviewed the WRAP CALMET fields for the 6 modeling 

domains and found them adequate for use by States initially in BART Calpuff modeling

applications. Several States have modified the WRAP CALMET analysis to account for site 

65 Kemball-Cook, S., Y. Jia, C. Emery and R. Morris. 2005. Alaska MM5 Modeling for the 2002 Annual Period to 

Support Visibility Modeling. Western Regional Air Partnership, Regional Modeling Center. 

66 Baker, K. 2004a. Summer MM5 Performance. Midwest Regional Planning Organization. 67 Baker, K. 2004b. Monthly Rainfall Evaluation. Midwest regional Planning Organization.
57

specific issues such as nearby terrain features in their BART modeling. The individual State 

regional haze plans provide details on these modifications.

Calpuff has been evaluated nationally by EPA and is considered appropriate for long range 

transport applications and no site specific performance evaluations were performed.

Review Comment (summary of EPA comments on WRAP BART Modeling): WRAP conducted 

initial subject-to-BART screening analysis modeling for seven western States to determine which 

sources contribute significantly to visibility impairment in Class I areas. The overall approach 

followed EPA guidance and modeling procedures in effect at the time of the analysis and is 

therefore acceptable for use in the current regional haze plans. In several of these States the 

WRAP modeling provided a foundation for more refined or supplemental BART–exemption 

modeling conducted by the individual States. A description and evaluation of these additional 

modeling analyses is contained in the individual Regional Haze SIPs submitted by each State.

8. Conclusions – WRAP Technical Work In Support of Regional Haze 

Planning

The technical work of the WRAP was consistent with the best practices and science at the time it 

was performed. It is appropriate for the western states to use it as the basis for complying with 

the requirements of the Regional Haze Rule.

The EPA’s evaluation of the WRAPs work is summarized below, organized by topic area.

A. Baseline Visibility Conditions

The WRAP followed EPA guidance and used IMPROVE program-calculated data in developing 

the baseline visibility conditions for the Class I areas in their region. They appropriately 

extended EPA-recommended methods in some cases in order to ensure a sufficiently large data 

set for determining baseline conditions.

B. Natural Visibility Conditions

WRAP used a refined method to estimate natural visibility conditions. The states are able to do 

this under the rule. WRAP’s method matches EPA’s general approach, but is more refined and 

statistically sophisticated than the default EPA procedure.

C. Source Contribution to Haze

i. PM Source Apportionment Technology Analysis: The PSAT analysis provides useful 

qualitative information about source contributions to visibility impairment. It is 

appropriate for states to consider the results of these analyses in their reasonable progress 

demonstrations.

ii. Weighted Emissions Potential Analysis: The Weighted Emissions Potential analysis is a 

simple integration of emissions and wind data. It is potentially useful as a screening tool, 
58

but has a number of limitations. It should be used only in conjunction with other 

methods.

iii. Organic Aerosol Tracer Analysis: The organic aerosol tracer analysis made a good case 

for the fact that there is a significant amount of uncontrollable, organic aerosols 

contributing to haze in the West. However, further analysis would be required in order to 

use this knowledge to change assumptions about natural conditions or to make the case 

that it is unreasonable to further control anthropogenic organic aerosols.

iv. Positive Matrix Factorization analysis: The positive matrix factorization analysis is 

useful for identifying source categories impacting class one areas as long as those source 

categories were evaluated in the analysis.

v. Causes of Dust Analysis: The Causes of Dust Analysis provides a reasonable account of 

transcontinental transport of dust. The techniques were more successful for spring events 

than other times of the year. If a particular Class I area is significantly impacted by coarse 

mass, this analysis could be useful it characterizing the extent to which those coarse mass 

impacts are controllable by the state.

D. Reasonable Progress Goals.

The reasonable progress goals are based on the WRAP’s projection of visibility impairment at 

Class I areas in 2018. This projection is derived from the results of the visibility modeling. The 

visibility modeling depends on the meteorological modeling and the emissions inventory.

Emissions Inventory: The emissions inventory was completed in accordance with EPA 

guidance and the final versions of the 2002 and 2018 inventories were complete, recent 

and accurate. The inventories should be adequate for all of the Class I areas covered by 

the WRAP analysis. 

i. Meteorological Modeling: The meteorological model and methodology used by WRAP 

was state-of-the-science at the time the modeling was conducted. The performance of the 

model was adequate for use in developing the reasonable progress goals. For future 

modeling, additional attention should be paid to performance in the desert southwest. The 

performance of the model could be further improved for this area.

ii. Visibility Modeling: The visibility model and methodology used by WRAP was state-ofthe-science at the time the modeling was conducted. The performance of the model was 

adequate for use in developing the reasonable progress goals. For future modeling, the 

performance should be analyzed at the geographic sub-region level. Evaluating 

performance over such a large area can mask performance problems at particular Class 1 

areas. Any sub-region performance analysis should examine the effects of any remaining 

performance concerns with the meteorological modeling. Given the broad geographic 

scope in the emissions inventories and of the model performance analysis, a more 

detailed analysis of the model performance and relevant emissions inventory source 

categories may be required for some Class I areas.
59

A. Appendix: Accessing WRAP Technical Products

The following text in italics is the executive summary of a document created by WRAP titled 

“TSS Roadmap and Users Guide”. The WRAP Technical Support System (TSS) is publicly 

accessible over the internet and is the primary method WRAP uses to convey technical products 

to State and Tribes in support of their RH SIP development efforts. 

The Western Regional Air Partnership‘s (WRAP)68 Technical Support System (TSS)69 is intended 

to present and disseminate technical regional haze planning results for the 116 Class I areas in 

the WRAP region on an ongoing basis, identify associated technical data and analysis products 

and resources, and describe how these data and results were derived and relate to one another. 

The TSS presents air quality indicators for regional haze using a “weight-of-evidence” approach 

as suggested in EPA guidance – monitoring, emissions, source apportionment, and visibility 

modeling results. The TSS data displays and analysis results are formatted to comply with the 

metrics and specific requirements in the EPA Regional Haze Rule (RHR)70 and are being used 

by states and EPA regional offices in the WRAP region, in the technical support documents for 

State or Federal Implementation Plans (SIPs and FIPs) required for each Class I areas under 

the RHR. The methodologies for technical data collection and processing, quality assurance and 

control, and analysis activities are documented on the TSS. 

Much of the data and regional analysis results on the TSS are also suitable, and have been used 

for other air quality analysis and planning purposes, because they were developed at the 

direction of, and through the collaborative committee and workgroup efforts within the WRAP 

organization. A significant amount of more detailed data and/or analysis results also exist in 

data support systems (VIEWS71. EDMS72, FETS73) or separate projects (RMC74, CoHA75), all of 

which support the TSS summary data developed to support RHR air quality planning. These 

systems or projects are listed, described, and linked to on the Projects section of the TSS. Also, 

many related reports for specific source sectors, analysis of regional impacts to Class I areas, 

and control strategy analyses are found under individual Committees, Forums, and Workgroups

on the WRAP website – access to these results are generally not found on the TSS due to their 

geographic- and/or source-specific nature, and may be applied by the appropriate regulatory 

jurisdiction for RHR planning as that agency wishes. This version of the WRAP website moved 

into archive status in early 2010, but the links will remain accessible.

On the TSS under the “Home” section, links to administrative and reference information are 

provided to assure feedback to keep the TSS current. Under the “Resources” section, 

descriptions, documentation, and access to tools and data are provided, for the Haze Planning, 

Monitoring, Emissions, Modeling, and Apportionment results required in a State or Federal 

 68 http://www.wrapair2.org/ to access http://www.wrapair.org 69 http://vista.cira.colostate.edu/tss/ 70 http://www.wrapair.org/RH_Rule_P51/index.html 71 http://views.cira.colostate.edu/web/ 72 http://www.wrapedms.org/ 73 http://www.wrapfets.org/ 74 http://pah.cert.ucr.edu/aqm/308/ 75 http://www.coha.dri.edu/
60

Implementation Plan under the Regional Haze Rule, as well as the subsequent implementation of 

those plans. 

Required Technical Elements on TSS for Regional Haze Implementation Plans

Monitored baseline visibility conditions (2000-04) for each Class I area, plus subsequent data

Natural conditions estimates for each Class I area (2064 target)

Uniform glide slope for each Class I area

Baseline emissions (2000-2004)

Projected 2018 emissions

Source apportionment at each Class I area for 2000-04 baseline and 2018 projections

Projected 2018 visibility conditions at each Class I area

The “Projects” section of the TSS provides brief descriptions of the data systems and projects, 

and links to the more detailed and voluminous results found in each system or project. Finally, 

the “Partners” section provides links to the team behind the TSS, including descriptions of their 

expertise and experience.

All data visualization and analysis tools on the TSS have integrated access to “Help” for the 

user, via the master TSS “Getting Started” document, an HTML page. The Monitoring, 

Emissions, Modeling, and Source Apportionment subsections contain detailed “Methods” 

descriptions, identifying how datasets and analysis tools were prepared and how they are used 

on the TSS.